data mining in decision making a multi rule algorithm



Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Data Analysis and Data Mining Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Data Analysis and Data Mining Новинка

Johnson Wayne P. Making Sense of Data I. A Practical Guide to Exploratory Data Analysis and Data Mining

6020.23 руб. или Купить в рассрочку!
Praise for the First Edition “…a well-written book on data analysis and data mining that provides an excellent foundation…” —CHOICE “This is a must-read book for learning practical statistics and data analysis…” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.
Saurabh Pal Data Mining Applications. A Comparative Study for Predicting Student.s Performance Saurabh Pal Data Mining Applications. A Comparative Study for Predicting Student.s Performance Новинка

Saurabh Pal Data Mining Applications. A Comparative Study for Predicting Student.s Performance

Doctoral Thesis / Dissertation from the year 2014 in the subject Computer Science - General, , course: DOCTOR OF PHILOSOPHY, language: English, abstract: The primary objective of this research is to develop a process to accurately predict useful data from the huge amount of available data using data mining techniques. Data Mining is the process of finding treads, patterns and correlations between fields in large RDBMS. It permits users to analyse and study data from multiple dimensions and approaches, classify it, and summarize identified data relationships. Our focus in this thesis is to use education data mining procedures to understand higher education system data better which can help in improving efficiency and effectiveness of education. In order to achieve a decisional database, many steps need to be taken which are explained in this thesis. This work investigates the efficiency, scalability, maintenance and interoperability of data mining techniques. In this research work, data-results obtained through different data mining techniques have been compiled and analysed using variety of business intelligence tools to predict useful data. An effort has also been made to identify ways to implement this useful data efficiently in daily decision process in the field of higher education in India.Mining in educational environment is called Educational Data Mining. Han and Kamber describes data mining software that allow the users to analyze data from different dimensions, categ...
Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications Новинка

Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications

8892.32 руб. или Купить в рассрочку!
Presents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering. Multicriteria Decision Aid and Artificial Intelligence: Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.
Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management Новинка

Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management

3859.12 руб. или Купить в рассрочку!
Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis
Carlo Vercellis Business Intelligence. Data Mining and Optimization for Decision Making Carlo Vercellis Business Intelligence. Data Mining and Optimization for Decision Making Новинка

Carlo Vercellis Business Intelligence. Data Mining and Optimization for Decision Making

14278.75 руб. или Купить в рассрочку!
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
Salma Bibi, Adeela Batool, Fatima Mustafa Design and Implementation of a Model to Predict the Success of the Bank Telemarketing Salma Bibi, Adeela Batool, Fatima Mustafa Design and Implementation of a Model to Predict the Success of the Bank Telemarketing Новинка

Salma Bibi, Adeela Batool, Fatima Mustafa Design and Implementation of a Model to Predict the Success of the Bank Telemarketing

Bachelor Thesis from the year 2015 in the subject Computer Science - Software, grade: A, , course: Final Year Project, language: English, abstract: Targeting customers is a major task of bank telemarketing to send their service to customers. Now banks are using a number of data mining techniques to predict the success rate. The Decision Tree is a successful data mining technique for predicting bank telemarketing success. The Decision Tree is a well known classifier and is simple and easy to apply. The performance of decision trees can be improved with appropriate attribute selection. In this research, ID3 decision tree technique of data mining is applied on widely used benchmark data set. The main focus of this research was on designing and implementation of a model that predicts the success of bank telemarketing using decision tree technique of data mining.
Meta Brown S. Data Mining For Dummies Meta Brown S. Data Mining For Dummies Новинка

Meta Brown S. Data Mining For Dummies

2250.51 руб. или Купить в рассрочку!
Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.
Adongoi Toakodi Workers Participation in Decision Making and Job Satisfaction in Organizations Adongoi Toakodi Workers Participation in Decision Making and Job Satisfaction in Organizations Новинка

Adongoi Toakodi Workers Participation in Decision Making and Job Satisfaction in Organizations

Scientific Study from the year 2010 in the subject Business economics - Personnel and Organisation, , language: English, abstract: This study aimed at evaluating the impact of worker's participation in the decision-making process on job satisfaction, industrial harmony, and productivity in organizations, with particular reference to the Universal Basic Education Board in Bayelsa State, Nigeria. Built around the survey design, two hundred and eighty-four (284) respondents were reached through a multi-stage sampling method and questionnaire was used as the instrument for collecting primary data, while relying on the library for secondary data. Data from the field was subjected to description by simple percentages, but drawing of inference was done through the use of the chi-square statistical test. Pivoted by the Achievement-Power-Affiliation motivation theory of David Mcclelland, the study acknowledges the difficulty in measuring productivity in this context. Data that emerge reveal that there is a direct relationship between the degree of workers' participation in the various organizational decision making processes and job satisfaction, industrial harmony, and productivity. The study, thus, concludes that if workers actively participate in the decision making processes of organization, certainly job satisfaction would breed industrial harmony, which ultimately would ensure higher productivity. To this end, the study recommends, among others, that industrial unions in...
Omorog Challiz STANA. A Decision Support System for Supermarkets Omorog Challiz STANA. A Decision Support System for Supermarkets Новинка

Omorog Challiz STANA. A Decision Support System for Supermarkets

The introduction of computerized technology particularly the electronic Point of Sales (POS) to supermarkets confronted marketing decision makers with vast resources of data. POS databases are multi-dimensional, comprised of monthly and daily transaction records, therefore time-consuming to analyze. This has led to a new desire on the part of managers to understand the behavioral demand for majority of customers and make better marketing decisions based on new variables. This research presents the use of data mining models, Kmeans clustering and Apriori Association rule, to construct pricing and store layout decision support system for retail or marketing managers.
Lin Jerry Chun-Wei Tree-based Algorithms for Incremental, Utility, and Fuzzy Data Mining Lin Jerry Chun-Wei Tree-based Algorithms for Incremental, Utility, and Fuzzy Data Mining Новинка

Lin Jerry Chun-Wei Tree-based Algorithms for Incremental, Utility, and Fuzzy Data Mining

In the first part of this book, three Pre-FUFP maintenance algorithms are thus proposed to efficiently maintain and update the FUFP-tree structures regardless of whether records are inserted, deleted or modified in dynamic databases. In the second part of this book, a novel HUP-tree algorithm is proposed to efficiently mine the high utility itemsets based on the downward closure property. A HUP tree is first designed to keep the related information for later mining process. A HUP-growth mining algorithm is then presented to efficiently mine high utility itemsets from it. In the third part of this book, we attempt to extend the FP-tree algorithm for handling quantitative data from the global values of fuzzy regions. Thus, the fuzzy FP-tree algorithm, the compressed fuzzy frequent pattern tree (CFFP-tree) algorithm, and the upper-bound fuzzy frequent pattern tree (UBFFP-tree) algorithm are then proposed to efficiently mine the fuzzy frequent itemsets.
Stéphane Tufféry Data Mining and Statistics for Decision Making Stéphane Tufféry Data Mining and Statistics for Decision Making Новинка

Stéphane Tufféry Data Mining and Statistics for Decision Making

7945.93 руб. или Купить в рассрочку!
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research Новинка

Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research

9880.92 руб. или Купить в рассрочку!
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Kanhaiya Lal Semantic Web Based Data Cloud Kanhaiya Lal Semantic Web Based Data Cloud Новинка

Kanhaiya Lal Semantic Web Based Data Cloud

Data mining is a treatment process to extract useful and interesting knowledge from large amount of data. The knowledge modes data mining discovered have a variety of different types. The common patterns are: association mode, classification model, class model, sequence pattern and so on. Mining association rules is one of the most important aspects in data mining. Association rules are dependency rules which predict occurrence of an item based on occurrences of other items. The process of building the Semantic Web is currently an area of high activity. Its structure has to be defined, and this structure then has to be filled with life. Cloud computing is a highly touted recent phenomenon. The cloud may move data or computation to improve responsiveness. Some clouds monitor their offerings for malicious activity Visualization. Hardware resources in clouds are usually Virtual; they are shared by multiple users to improve efficiency. This book deals technique of association rules mining in semantic web based data cloud. KEY FEATURES: Explains the basic knowledge of cloud computing, Data & web mining. Provides Concept of Association rules & Algorithm for mining Association Rules.
Enrico Seib Data Mining - Methoden in der Simulation Enrico Seib Data Mining - Methoden in der Simulation Новинка

Enrico Seib Data Mining - Methoden in der Simulation

Bachelorarbeit aus dem Jahr 2008 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,0, Universität Rostock (Institut für Informatik, Lehrstuhl für Modellierung und Simulation), 100 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Principles and methods of data mining are a widespread area, i.e. retail dealer use data mining tools to analyze the behavior of customers, computer hardware supplier use data mining to optimize their inventory. There are multiple possibilities of using data mining techniques, even in technical and scientific areas of applications. In regard of manyfold fields of application, there are no less than the number of techniques and methods for Data Mining in existence. Another field to apply Data Mining technique is the domain of simulation. Simulation is the computer-based approach of executing and experimenting of and with models. One aim of this thesis is to analyze data mining tools to see how capable they are solving data mining duties with respect to data calculated by simulation. Different data mining tools are analyzed, commercial tools like SPSS and SPSS Clementine as well as established and freely available tools like WEKA and the R-Project. These tools are analyzed in matters of their data mining functionalities, options to access different data sources, and their complexity of different data mining algorithms. Beyond the analysis of data mining tools with respect to functionality and simulation, envi-ronments for modeling a...
Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management Новинка

Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management

3215.94 руб. или Купить в рассрочку!
The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Russell Anderson K. Visual Data Mining. The VisMiner Approach Russell Anderson K. Visual Data Mining. The VisMiner Approach Новинка

Russell Anderson K. Visual Data Mining. The VisMiner Approach

6483.32 руб. или Купить в рассрочку!
A visual approach to data mining. Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data. Key features: Presents visual support for all phases of data mining including dataset preparation. Provides a comprehensive set of non-trivial datasets and problems with accompanying software. Features 3-D visualizations of multi-dimensional datasets. Gives support for spatial data analysis with GIS like features. Describes data mining algorithms with guidance on when and how to use. Accompanied by VisMiner, a visual software tool for data mining, developed specifically to bridge the gap between theory and practice. Visual Data Mining: The VisMiner Approach is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes. In praise of the VisMiner approach: «What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes» —Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA «Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.» —Dr. Douglas Dean, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA
Jerzy Surma Business Intelligence. Making Decisions Through Data Analytics Jerzy Surma Business Intelligence. Making Decisions Through Data Analytics Новинка

Jerzy Surma Business Intelligence. Making Decisions Through Data Analytics

This book is about using business intelligence as a management informationsystem for supporting managerial decision making. It concentratesmainly on practical business issues and demonstrates how to apply datawarehousing and data analytics to support decision making. This bookprogresses through a logical sequence, starting with data model infrastructure,then data preparation, followed by data analysis, integration,knowledge discovery, and fi nally the actual use of discovered knowledge.All examples are based on the newest achievements in business intelligence(BI). Finally, this book outlines an overview of a methodology thattakes into account the complexity of developing applications in an integratedBI environment.
Antonios Chorianopoulos Effective CRM using Predictive Analytics Antonios Chorianopoulos Effective CRM using Predictive Analytics Новинка

Antonios Chorianopoulos Effective CRM using Predictive Analytics

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.
Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data Новинка

Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data

14212.43 руб. или Купить в рассрочку!
The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.
Al Atrash Abdel Rahim Robust Data Envelopment Analysis model Al Atrash Abdel Rahim Robust Data Envelopment Analysis model Новинка

Al Atrash Abdel Rahim Robust Data Envelopment Analysis model

This book provides a robust statistical procedures for evaluating and measuring the relative efficiency of multiple decision making units. The robust approach is based on the generalized maximum entropy for finding superior decision making unit (DMU) by solving only one nonlinear programming system. A real data application on research performance of faculty members at Yarmouk University is presented.
David L. Olson Data Mining Models David L. Olson Data Mining Models Новинка

David L. Olson Data Mining Models

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.
Jared Dean Big Data, Data Mining, and Machine Learning. Value Creation for Business Leaders and Practitioners Jared Dean Big Data, Data Mining, and Machine Learning. Value Creation for Business Leaders and Practitioners Новинка

Jared Dean Big Data, Data Mining, and Machine Learning. Value Creation for Business Leaders and Practitioners

3859.12 руб. или Купить в рассрочку!
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
Giudici Paolo Applied Data Mining for Business and Industry Giudici Paolo Applied Data Mining for Business and Industry Новинка

Giudici Paolo Applied Data Mining for Business and Industry

14278.75 руб. или Купить в рассрочку!
The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.
Mehmed Kantardzic Data Mining. Concepts, Models, Methods, and Algorithms Mehmed Kantardzic Data Mining. Concepts, Models, Methods, and Algorithms Новинка

Mehmed Kantardzic Data Mining. Concepts, Models, Methods, and Algorithms

9802.17 руб. или Купить в рассрочку!
This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: [email protected]
Hengqing Tong Developing Econometrics Hengqing Tong Developing Econometrics Новинка

Hengqing Tong Developing Econometrics

9652.34 руб. или Купить в рассрочку!
Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.
El Amir Eman Web Services Approach for GeoSpatial Data Mining El Amir Eman Web Services Approach for GeoSpatial Data Mining Новинка

El Amir Eman Web Services Approach for GeoSpatial Data Mining

Geospatial Data Mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. GIS and Data Mining are naturally synergistic technologies that can be synthesized to produce powerful market insight from a sea of disparate data. This book describes a research that developed a Spatial Data Mining Web Service. It integrates state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. within the book you will learn that one analysis can often lead into others, parameters for tools may change, criteria for analyses can evolve, or you may want to perform additional visual analysis on the results to make them more meaningful or easier to interpret.
Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation Новинка

Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation

7560.02 руб. или Купить в рассрочку!
This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.
Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining Новинка

Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining

7174.11 руб. или Купить в рассрочку!
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Jeffrey Herrmann W. Engineering Decision Making and Risk Management Jeffrey Herrmann W. Engineering Decision Making and Risk Management Новинка

Jeffrey Herrmann W. Engineering Decision Making and Risk Management

9043.75 руб. или Купить в рассрочку!
IIE/Joint Publishers Book of the Year Award 2016! Awarded for ‘an outstanding published book that focuses on a facet of industrial engineering, improves education, or furthers the profession’. Engineering Decision Making and Risk Management emphasizes practical issues and examples of decision making with applications in engineering design and management Featuring a blend of theoretical and analytical aspects, this book presents multiple perspectives on decision making to better understand and improve risk management processes and decision-making systems. Engineering Decision Making and Risk Management uniquely presents and discusses three perspectives on decision making: problem solving, the decision-making process, and decision-making systems. The author highlights formal techniques for group decision making and game theory and includes numerical examples to compare and contrast different quantitative techniques. The importance of initially selecting the most appropriate decision-making process is emphasized through practical examples and applications that illustrate a variety of useful processes. Presenting an approach for modeling and improving decision-making systems, Engineering Decision Making and Risk Management also features: Theoretically sound and practical tools for decision making under uncertainty, multi-criteria decision making, group decision making, the value of information, and risk management Practical examples from both historical and current events that illustrate both good and bad decision making and risk management processes End-of-chapter exercises for readers to apply specific learning objectives and practice relevant skills A supplementary website with instructional support material, including worked solutions to the exercises, lesson plans, in-class activities, slides, and spreadsheets An excellent textbook for upper-undergraduate and graduate students, Engineering Decision Making and Risk Management is appropriate for courses on decision analysis, decision making, and risk management within the fields of engineering design, operations research, business and management science, and industrial and systems engineering. The book is also an ideal reference for academics and practitioners in business and management science, operations research, engineering design, systems engineering, applied mathematics, and statistics.
Dashore Pankaj, Dashore Rachna Risk Management Through Fuzzy Logic Dashore Pankaj, Dashore Rachna Risk Management Through Fuzzy Logic Новинка

Dashore Pankaj, Dashore Rachna Risk Management Through Fuzzy Logic

This book focuses on risk management through fuzzy logic for uncertainty in Share Market, Business Decision, Customer Decision in Internet Marketing, and Cloud computing. Risk management is a process of identifying, assessment, and prioritization of risk in business. The goal of risk management is to protect business from unexpected results. Risk management also use to protect employees, customers from negative results. Fuzzy logic was used since it is a tool capable of modeling complex and uncertain or vague data using simple terminology such as IF-Then statements. Fuzzy rule based expert system is very effective for knowledge representation of any system. It is an essential tool which balances the economic, social and environmental benefits The fuzzy rule base can be use to optimize the E-Commerce services and rule discovery facilities and could be used to optimize policy knowledge. E-Commerce rule based fuzzy models provide a powerful and robust tool for encapsulating and exploiting knowledge. This book will help the customer for decision making in share market, where buying and selling of shares takes place from anywhere, wherever with the help of internet connected computer.
Clinton W. Brownley Multi-Objective Decision Analysis. Managing Trade-Offs and Uncertainty Clinton W. Brownley Multi-Objective Decision Analysis. Managing Trade-Offs and Uncertainty Новинка

Clinton W. Brownley Multi-Objective Decision Analysis. Managing Trade-Offs and Uncertainty

Whether managing strategy, operations or products, knowing how to make the best decision in a complex, uncertain business environment is difficult. You might be faced with multiple, competing objectives, which means making trade-offs. To complicate matters, any uncertainty makes it hard to explicitly understand how different objectives will impact potential outcomes. This book will help you face these problems. It provides a decision analysis framework implemented as a simple spreadsheet tool. This multi-objective decision analysis framework helps you to measure trade-offs among objectives and incorporate uncertainties and risk preferences. With this book, you will be able to identify what information is needed to make a decision, define how that information should be combined, and, finally, provide quantifiable evidence to clearly communicate and justify the decision. The process involves minimal overhead and is perfect for busy professionals who need a simple, structured process for making, tracking, and communicating decisions. This process makes decision making more efficient by focusing only on information and factors that are well-defined, measureable, and relevant to the decision at hand. The framework requires clear characterization of a decision, ensuring that it can be traced and is consistent with the intended objectives and organizational values. Using this structured decision-making framework, anyone can consistently make better decisions to gain competitive and ...
Nutt Paul C. Handbook of Decision Making Nutt Paul C. Handbook of Decision Making Новинка

Nutt Paul C. Handbook of Decision Making

9261.89 руб. или Купить в рассрочку!
Wiley's new Handbook of Decision Making is a vital reference text for all students and professionals of management, organization and decision making. The handbook offers a wide range of theoretical and empirical approaches to the understanding of organizational and strategic decisions. Contributors are internationally known experts drawn from North America, Canada and Europe who have spent many years in the study of decision making, and decision making relevant topics. We believe the handbook will become a tour de force in the understanding decision making, offering a wide variety of perspectives, topics, and summative understanding of the field. Chapters in the Handbook were prepared by the leading experts in their field and include cutting edge empirical, theoretical, and review chapters. The chapters bring together for the first time a critical mass of writing on decision making as an organizational and research activity. The Editors are two of the leading international experts in decision making and contribute to the Handbook with five original Chapters that offer an appraisal of the field and suggestions for research, as well as the current status of decision making practice and suggestion for improvement.
Blessing Adegoke Influence of teachers. participation in decision making on job productivity in secondary schools Blessing Adegoke Influence of teachers. participation in decision making on job productivity in secondary schools Новинка

Blessing Adegoke Influence of teachers. participation in decision making on job productivity in secondary schools

Scientific Study from the year 2010 in the subject Pedagogy - Job Education, Occupational Training, Further Education, grade: 1.0, Bowen University, language: English, abstract: This study was designed to assess the participation of teachers in school decision-making and its influence on their decision-making and its influence on their job satisfaction and productivity.The sample of the study comprised of 96 teachers and principals of six senior secondary schools in Mainland Local Government area of Lagos State. A designed research instrument was used to generate relevant data for the study- The data were tested using percentage and Chi-square statistical tools. Three null hypotheses were tested in the study which revealed that teachers' participation in school decision making has significant relationship on their job productivity; principals' leadership styles have significant relationship on teachers' involvement in school decision-making, management effectiveness has significant influence on job productivity in schools. Based on the findings, some recommendations were made to the principals to encourage teachers to participate in important school discussions that will motivate them to develop a sense of belongingness to the organizations and enhance their job productivity.
John Tanner F. Analytics and Dynamic Customer Strategy. Big Profits from Big Data John Tanner F. Analytics and Dynamic Customer Strategy. Big Profits from Big Data Новинка

John Tanner F. Analytics and Dynamic Customer Strategy. Big Profits from Big Data

3212.72 руб. или Купить в рассрочку!
Key decisions determine the success of big data strategy Dynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance. Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include: Applying the elements of Dynamic Customer Strategy Acquiring, mining, and analyzing data Metrics and models for big data utilization Shifting perspective from model to customer Big data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.
Pawel Cichosz Data Mining Algorithms. Explained Using R Pawel Cichosz Data Mining Algorithms. Explained Using R Новинка

Pawel Cichosz Data Mining Algorithms. Explained Using R

6080.12 руб. или Купить в рассрочку!
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Angela Moore Human Resources Management Decision Making Process in King Saud Medical City Angela Moore Human Resources Management Decision Making Process in King Saud Medical City Новинка

Angela Moore Human Resources Management Decision Making Process in King Saud Medical City

Bachelor Thesis from the year 2012 in the subject Business economics - Personnel and Organisation, grade: B, University of Sheffield, language: English, abstract: This research study examines the human resources management decision making process in addressing the present and future needs of King Saud Medical City. The main purpose of the study is to investigate the readiness of healthcare managers in meeting present and future challenges faced by the human resources decision making process in healthcare organizations. King Saud Medical City was selected as a model healthcare organization to gain understanding of the HR decision making process for this study. This research adopts grounded theory methodology in establishing tactical decisions being made by health managers. Grounded theory is an appropriate qualitative tool for this purpose because it emphasizes on systematic procedures in understanding critical HR factors impacting decision making and description of the phenomenon as grounded within study participant expressions. The participants in this study comprises of 12 managers from Kind Saud Medical City with experience in healthcare management. A semi-structured interview method was used for the collection of the data. Constant comparative approach was used in the analysis of the interview transcripts by coding and analyzing participant's expressions in order to identify categories and themes related to human resource decision making in healthcare organizations. R...
Ian Cox Visual Six Sigma. Making Data Analysis Lean Ian Cox Visual Six Sigma. Making Data Analysis Lean Новинка

Ian Cox Visual Six Sigma. Making Data Analysis Lean

4180.72 руб. или Купить в рассрочку!
Streamline data analysis with an intuitive, visual Six Sigma strategy Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual knowledge to pose relevant questions and make more sound decisions. You'll learn dynamic visualization and exploratory data analysis techniques that help you identify occurrences and sources of variation, and the strategies and processes that make Six Sigma work for your organization. The Six Sigma strategy helps you identify and remove causes of defects and errors in manufacturing and business processes; the more pragmatic Visual approach opens the strategy beyond the realms of statisticians to provide value to all business leaders amid the growing need for more accessible quality management tools. See where, why, and how your data varies Find clues to underlying behavior in your data Identify key models and drivers Build your own Six-Sigma experience Whether your work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the skills and understanding to get more from your data. With intuitive, easy-to-use tools and clear explanations, Visual Six Sigma is a roadmap to putting this strategy to work for your company.
Hina Kanth, Aiman Mushtaq, Rafi Ahmad Khan Data Mining for Marketing Hina Kanth, Aiman Mushtaq, Rafi Ahmad Khan Data Mining for Marketing Новинка

Hina Kanth, Aiman Mushtaq, Rafi Ahmad Khan Data Mining for Marketing

Research Paper (postgraduate) from the year 2015 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, The University of Kashmir, language: English, abstract: This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation.
Pascale Zarate Tools for Collaborative Decision-Making Pascale Zarate Tools for Collaborative Decision-Making Новинка

Pascale Zarate Tools for Collaborative Decision-Making

5634.32 руб. или Купить в рассрочку!
Decision-making has evolved recently thanks to the introduction of information and communication technologies in many organizations, which has led to new kinds of decision-making processes, called “collaborative decision-making”, at the organizational and cognitive levels. This book looks at the development of the decision-making process in organizations. Decision-aiding and its paradigm of problem solving are defined, showing how decision-makers now need to work in a cooperative way. Definitions of cooperation and associated concepts such as collaboration and coordination are given and a framework of cooperative decision support systems is presented, including intelligent DSS, cooperative knowledge-based systems, workflow, group support systems, collaborative engineering, integrating with a collaborative decision-making model in part or being part of global projects. Several models and experimental studies are also included showing that these new processes have to be supported by new types of tools, several of which are described in order to calculate or simulate solutions or global solutions for decision-making modification. Definitions and new trends for these models are given, along with types of systems. Contents 1. Alteration of Decision-Making Processes in Organizations. 2. New Decision-Making Processes. 3. The Need to Cooperate. 4. Cooperative Decision-Making. 5. Activity Support Systems. 6. Cooperative Decision Support Systems: CDSSS. About the Authors Pascale Zaraté is Professor at Toulouse 1 Capitole University, France. She conducts her research at the IRIT Laboratory and is the Editor-in-Chief of the International Journal of Decision Support Systems Technologies. She is co-chair of the European Working Group on DSS and has published several studies and books.
Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting Новинка

Nawaz Aamir Nelder Mead Trained Neural Networks For Short Term Load Forecasting

This book proposes a new optimization algorithm for solving short term load forecasting problem. Globalized Nelder Mead is used for training of Artificial Neural Networks. Nelder Mead is fast optimization algorithm with no gradient calculation. The weights of Neural Networks are tuned with the help of Nelder Mead algorithm. To find proficiency of this algorithm, Australian Energy Market Operator (AEMO) data and California data are taken for testing. Results show that proposed algorithm outclasses other techniques in literature.
Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro Новинка

Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro

10260.93 руб. или Купить в рассрочку!
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Wu George The Wiley Blackwell Handbook of Judgment and Decision Making Wu George The Wiley Blackwell Handbook of Judgment and Decision Making Новинка

Wu George The Wiley Blackwell Handbook of Judgment and Decision Making

30487.06 руб. или Купить в рассрочку!
A comprehensive, up-to-date examination of the most important theory, concepts, methodological approaches, and applications in the burgeoning field of judgment and decision making (JDM) Emphasizes the growth of JDM applications with chapters devoted to medical decision making, decision making and the law, consumer behavior, and more Addresses controversial topics from multiple perspectives – such as choice from description versus choice from experience – and contrasts between empirical methodologies employed in behavioral economics and psychology Brings together a multi-disciplinary group of contributors from across the social sciences, including psychology, economics, marketing, finance, public policy, sociology, and philosophy 2 Volumes
Virginija Kliukinskaite Vigil Individual Decision Making Process Related to Ethical Issues Virginija Kliukinskaite Vigil Individual Decision Making Process Related to Ethical Issues Новинка

Virginija Kliukinskaite Vigil Individual Decision Making Process Related to Ethical Issues

The Master thesis is based on a survey conducted among top managers working in local as well as global companies in Sweden, Lithuania, and Thailand, and presents research about individual decision making process related to ethical issues in business organizations. Having a goal to help the ones involved in business ethics training correctly determine and direct their efforts to the causes of unethical behavior in order to minimize the occurrence of unethical behavior in business practices, the work presents an analysis of the factors that have influence on managers' individual decision making process related to ethical issues. It also proposes an extended model of decision making process that encompasses the most influential factors that affect decision making process related to ethical issues or dilemmas.
Dawna Jones Decision Making For Dummies Dawna Jones Decision Making For Dummies Новинка

Dawna Jones Decision Making For Dummies

1735.96 руб. или Купить в рассрочку!
Discover the best approaches for making business decisions Today's business leaders have to face the facts—you can't separate leadership from decision making. The importance of making decisions, no matter how big or small, cannot be overstated. Decision Making For Dummies is a candid resource that helps leaders understand the impact of their choices, not only on business, but also on their credibility and reputation. Designed for managers, business owners, and anyone else who makes tough decisions on a daily basis, this guide helps you figure out if the decisions you're making are the right ones. In addition to helping you explore how to evaluate your choices, Decision Making For Dummies covers ways to receive support for decision making, delves into various decision-making styles, reviews the importance of sifting through data and information, and includes information on ways to engage others and make decisions collectively. Being in charge can be challenging, but with this guide, you don't have to go it alone. Discusses the effects of decision making and outlines the considerations that must be made to gain trust and confidence Demonstrates ways to communicate particularly sensitive decisions, and offers approaches for making bold decisions that challenge the status quo Delves into the risks and benefits of certain decisions, and shows readers the best ways to evaluate choices Outlines smart strategies for engaging others and drawing them into the decision-making process Crucial decisions need to be made every day in the business world, so there's no time to waste. Make Decision Making For Dummies your primary resource for learning to choose your actions wisely and confidently.
Ekins Sean Pharmaceutical Data Mining. Approaches and Applications for Drug Discovery Ekins Sean Pharmaceutical Data Mining. Approaches and Applications for Drug Discovery Новинка

Ekins Sean Pharmaceutical Data Mining. Approaches and Applications for Drug Discovery

12349.19 руб. или Купить в рассрочку!
Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.
Jean-Charles Pomerol Decision Making and Action Jean-Charles Pomerol Decision Making and Action Новинка

Jean-Charles Pomerol Decision Making and Action

10728.36 руб. или Купить в рассрочку!
Making a decision, of any importance, is never simple. On the one hand, specialists in decision theory do not come within the reach of most policy makers and, secondly, there are very few books on pragmatic decision that are not purely anecdotal. In addition, there is virtually no book that provides a link between decision-making and action. This book provides a bridge between the latest results in artificial intelligence, neurobiology, psychology and decision-making for action. What is the role of intuition or emotion? What are the main psychological biases of which we must be wary? How can we avoid being manipulated? What is the proper use of planning? How can we remain rational even if one is not an expert in probabilities? Perhaps more importantly for managers, how does one go from decision to action? So many questions fundamental to the practice of decision-making are addressed. This book dissects all issues that arise almost daily for decision-makers, at least for major decisions. Drawing on numerous examples, this book answers, in plain language and imagery, all your questions. The final chapter takes the form of a brief reminder – everything you have to remember to be a good decision-maker.
John Silvia E. Dynamic Economic Decision Making. Strategies for Financial Risk, Capital Markets, and Monetary Policy John Silvia E. Dynamic Economic Decision Making. Strategies for Financial Risk, Capital Markets, and Monetary Policy Новинка

John Silvia E. Dynamic Economic Decision Making. Strategies for Financial Risk, Capital Markets, and Monetary Policy

4502.31 руб. или Купить в рассрочку!
A comprehensive analysis of the macroeconomic and financial forces altering the economic landscape Financial decision-making requires one to anticipate how their decision will not only affect their business, but also the economic environment. Unfortunately, all too often, both private and public sector decision-makers view their decisions as one-off responses and fail to see their decisions within the context of an evolving decision-making framework. In Decision-Making in a Dynamic Economic Setting, John Silvia, Chief Economist of Wells Fargo and one of the top 5 economic forecasters according to Bloomberg News and USA Today, skillfully puts this discipline in perspective. Details realistic, decision-making approaches and applications under a broad set of economic scenarios Analyzes monetary policy and addresses the impact of financial regulations Examines business cycles and how to identify economic trends, how to deal with uncertainty and manage risk, the building blocks of growth, and strategies for innovation Decision-Making in a Dynamic Economic Setting details the real-world application of economic principles and financial strategy in making better business decisions.
Dimitar Kondev Multi-Party and Multi-Contract Arbitration in the Construction Industry Dimitar Kondev Multi-Party and Multi-Contract Arbitration in the Construction Industry Новинка

Dimitar Kondev Multi-Party and Multi-Contract Arbitration in the Construction Industry

10028.74 руб. или Купить в рассрочку!
Multi-Party and Multi-Contract Arbitration in the Construction Industry provides the first detailed review of multi-party arbitration in the international construction sector. Highly practical in approach, the detailed interpretation and assessment of the arbitration of multi-party disputes will facilitate understanding and decision making by arbitrators, clients and construction contractors.
B. G. Geetha, Kanmani, Dr B. G. Geetha Hybrid Approach for Effective Testdata Trade-Off for Software Testing B. G. Geetha, Kanmani, Dr B. G. Geetha Hybrid Approach for Effective Testdata Trade-Off for Software Testing Новинка

B. G. Geetha, Kanmani, Dr B. G. Geetha Hybrid Approach for Effective Testdata Trade-Off for Software Testing

Testing is a process used to identify quality of developed computer software.One of the important activity in testing environment is automatic test case generation, independent of the way a given software system is designed. This project presents a fusion approach in functional testing for generating test cases using genetic neural networks. The system is aimed to carry out the test data generation process for functional testing. The paper explains how the method can be used to produce a set of test cases covering the most common functional existing in software automatically. Test case inputs are generated randomly and the valid inputs are selected for the proper output. The association rule mining techniques are used to validate the generated data sets. The genetic algorithm is used to generate data values for the test cases. The testing process should be done in a way to scrutinize the faults still. The generated test data is validated arithmetically. This approach is applicable for systems in which large amount of input/output data is available.
Serena Smith Application on Human Relations Theory in Primary Schools Serena Smith Application on Human Relations Theory in Primary Schools Новинка

Serena Smith Application on Human Relations Theory in Primary Schools

Bachelor Thesis from the year 2010 in the subject Pedagogy - School Pedagogics, grade: 1.0, National Open University of Nigeria, language: English, abstract: This study was designed to assess the participation of teachers in school decision-making and its influence on their decision-making and its influence on their job satisfaction and productivity.The sample of the study comprised of 96 teachers and principals of six senior secondary schools in Mainland Local Government area of Lagos State. A designed research instrument was used to generate relevant data for the study- The data were tested using percentage and Chi-square statistical tools. Three null hypotheses were tested in the study which revealed that teachers' participation in school decision making has significant relationship on their job productivity; principals' leadership styles have significant relationship on teachers' involvement in school decision-making, management effectiveness has significant influence on job productivity in schools. Based on the findings, some recommendations were made to the principals to encourage teachers to participate in important school discussions that will motivate them to develop a sense of belongingness to the organizations and enhance their job productivity.
Using Data in Schools to Inform Leadership and Decision Making Using Data in Schools to Inform Leadership and Decision Making Новинка

Using Data in Schools to Inform Leadership and Decision Making

A volume in International Research on School LeadershipSeries Editors Alan R. Shoho and Bruce G. Barnett, University of Texas at San AntonioOur fifth book in the International Research on School Leadership series focuses on the use of data in schoolsand districts as useful information for leadership and decision making. Schools are awash in data andinformation, from test scores, to grades, to discipline reports, and attendance as just a short list of studentinformation sources, while additional streams of data feed into schools and districts from teachers andparents as well as local, regional and national policy levels. To deal with the data, schools have implementeda variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leadersare leveraging an analysis of their school's data as a means to bring about continuous improvement in an effort to improve instruction for all students.Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors whoexamine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues inthis domain, including:• How educational leaders use data to inform their practice.• What types of data and data analysis are most useful to successful school leaders.• To what extent are data driven and data informed p...
James G. March Primer on Decision Making. How Decisions Happen James G. March Primer on Decision Making. How Decisions Happen Новинка

James G. March Primer on Decision Making. How Decisions Happen

Building on lecture notes from his acclaimed course at Stanford University, James March provides a brilliant introduction to decision making, a central human activity fundamental to individual, group, organizational, and societal life. March draws on research from all the disciplines of social and behavioral science to show decision making in its broadest context. By emphasizing how decisions are actually made -- as opposed to how they should be made -- he enables those involved in the process to understand it both as observers and as participants.March sheds new light on the decision-making process by delineating four deep issues that persistently divide students of decision making: Are decisions based on rational choices involving preferences and expected consequences, or on rules that are appropriate to the identity of the decision maker and the situation? Is decision making a consistent, clear process or one characterized by ambiguity and inconsistency? Is decision making significant primarily for its outcomes, or for the individual and social meanings it creates and sustains? And finally, are the outcomes of decision processes attributable solely to the actions of individuals, or to the combined influence of interacting individuals, organizations, and societies? March's observations on how intelligence is -- or is not -- achieved through decision making, and possibilities for enhancing decision intelligence, are also provided.March explains key concepts of vital impo...
Jagdish Chandra Patni, Ravi Tomar, Hitesh Kumar Sharma Data Mining to Business Analytics. Finance, Budgeting and Investments Jagdish Chandra Patni, Ravi Tomar, Hitesh Kumar Sharma Data Mining to Business Analytics. Finance, Budgeting and Investments Новинка

Jagdish Chandra Patni, Ravi Tomar, Hitesh Kumar Sharma Data Mining to Business Analytics. Finance, Budgeting and Investments

Academic Paper from the year 2017 in the subject Computer Science - General, grade: 5, University of Petroleum and Energy Studies, language: English, abstract: This paper utilizes the distinctive mining techniques as an answer for business needs. It presents Finance, Budgeting and Investments as the principle working ground for the data mining algorithms actualized.With the increment of monetary globalization and development of information technology, financial data are being produced and gathered at an extraordinary pace. Thus, there has been a basic requirement for automated ways to deal with compelling and proficient usage of gigantic measure of data to support companies and people in doing the Business.Data mining is turning out to be strategically imperative region for some business associations including financial sector. Data mining helps the companies to search for hidden example in a gathering and find obscure relationship in the data. Financial Analysis alludes to the assessment of a business to manage the arranging, budgeting, observing, forecasting, and enhancing of every financial point of interest inside of an association. The task concentrates on comprehension the association's financial health as a major part of reacting to today's inexorably stringent financial reporting prerequisites. It exhibits the capacity of the data mining to robotize the procedure of looking the boundless customer's connected data to discover patterns that are great indicat...
Gregory Parnell S. Decision Making in Systems Engineering and Management Gregory Parnell S. Decision Making in Systems Engineering and Management Новинка

Gregory Parnell S. Decision Making in Systems Engineering and Management

11172.37 руб. или Купить в рассрочку!
Decision Making in Systems Engineering and Management is a comprehensive textbook that provides a logical process and analytical techniques for fact-based decision making for the most challenging systems problems. Grounded in systems thinking and based on sound systems engineering principles, the systems decisions process (SDP) leverages multiple objective decision analysis, multiple attribute value theory, and value-focused thinking to define the problem, measure stakeholder value, design creative solutions, explore the decision trade off space in the presence of uncertainty, and structure successful solution implementation. In addition to classical systems engineering problems, this approach has been successfully applied to a wide range of challenges including personnel recruiting, retention, and management; strategic policy analysis; facilities design and management; resource allocation; information assurance; security systems design; and other settings whose structure can be conceptualized as a system.
Klaus Schöfer Word-of-Mouth. Influences on the choice of Recommendation Sources Klaus Schöfer Word-of-Mouth. Influences on the choice of Recommendation Sources Новинка

Klaus Schöfer Word-of-Mouth. Influences on the choice of Recommendation Sources

Inhaltsangabe:Abstract: The idea of understanding consumer behaviour as a sequential decision-making process is one that is common in marketing. The decision-making process itself is presented as a logical flow of activities, working from problem recognition to purchase to post-purchase evaluation. This decision-making process is affected by a number of other more complex influences. Some of these influences relate to the wider environment in which the decision is being made while others relate to the individual who makes the decision. In this context, „.. [o]ne of the most widely accepted notions in consumer behavior is that word-of-mouth communication (hereafter WOM) plays an important role in shaping consumers' attitudes and behaviors.“ More specifically, WOM communications between consumers are a topic of interest in both the pre-purchase and post-purchase decision-making literature. Research into the diffusion of innovations has focused on modelling the role of WOM in product adoption at various stages of the diffusion process. WOM has also been studied as a mechanism through which consumers convey both informational and normative influences in the product evaluation. Finally, WOM has been identified as an important post-purchase complaining option. Although WOM plays an important role in consumer pre-purchase and post-purchase decision-making, research into this phenomenon has been fragmented. Importantly, relatively little attention has been directed at understandi...
Michael Conroy J. Decision Making in Natural Resource Management. A Structured, Adaptive Approach Michael Conroy J. Decision Making in Natural Resource Management. A Structured, Adaptive Approach Новинка

Michael Conroy J. Decision Making in Natural Resource Management. A Structured, Adaptive Approach

12536.64 руб. или Купить в рассрочку!
This book is intended for use by natural resource managers and scientists, and students in the fields of natural resource management, ecology, and conservation biology, who are confronted with complex and difficult decision making problems. The book takes readers through the process of developing a structured approach to decision making, by firstly deconstructing decisions into component parts, which are each fully analyzed and then reassembled to form a working decision model. The book integrates common-sense ideas about problem definitions, such as the need for decisions to be driven by explicit objectives, with sophisticated approaches for modeling decision influence and incorporating feedback from monitoring programs into decision making via adaptive management. Numerous worked examples are provided for illustration, along with detailed case studies illustrating the authors’ experience in applying structured approaches. There is also a series of detailed technical appendices. An accompanying website provides computer code and data used in the worked examples. Additional resources for this book can be found at: www.wiley.com/go/conroy/naturalresourcemanagement.
Jason Bell Machine Learning. Hands-On for Developers and Technical Professionals Jason Bell Machine Learning. Hands-On for Developers and Technical Professionals Новинка

Jason Bell Machine Learning. Hands-On for Developers and Technical Professionals

3800.08 руб. или Купить в рассрочку!
Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Denis Bouyssou Decision Making Process. Concepts and Methods Denis Bouyssou Decision Making Process. Concepts and Methods Новинка

Denis Bouyssou Decision Making Process. Concepts and Methods

17936.79 руб. или Купить в рассрочку!
This book provides an overview of the main methods and results in the formal study of the human decision-making process, as defined in a relatively wide sense. A key aim of the approach contained here is to try to break down barriers between various disciplines encompassed by this field, including psychology, economics and computer science. All these approaches have contributed to progress in this very important and much-studied topic in the past, but none have proved sufficient so far to define a complete understanding of the highly complex processes and outcomes. This book provides the reader with state-of-the-art coverage of the field, essentially forming a roadmap to the field of decision analysis. The first part of the book is devoted to basic concepts and techniques for representing and solving decision problems, ranging from operational research to artificial intelligence. Later chapters provide an extensive overview of the decision-making process under conditions of risk and uncertainty. Finally, there are chapters covering various approaches to multi-criteria decision-making. Each chapter is written by experts in the topic concerned, and contains an extensive bibliography for further reading and reference.
Ken Langdon Smart Things to Know About Decision Making Ken Langdon Smart Things to Know About Decision Making Новинка

Ken Langdon Smart Things to Know About Decision Making

1283.16 руб. или Купить в рассрочку!
Decision trees or backing a hunch – smart advice on the art and science of decision making.
Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual Новинка

Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual

2089.68 руб. или Купить в рассрочку!
Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

кешбака
Страницы:


Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Продажа data mining in decision making a multi rule algorithm лучших цены всего мира
Посредством этого сайта магазина - каталога товаров мы очень легко осуществляем продажу data mining in decision making a multi rule algorithm у одного из интернет-магазинов проверенных фирм. Определитесь с вашими предпочтениями один интернет-магазин, с лучшей ценой продукта. Прочитав рекомендации по продаже data mining in decision making a multi rule algorithm легко охарактеризовать производителя как превосходную и доступную фирму.