data mining for multimedia databases



Thomas Hammergren C. Data Warehousing For Dummies Thomas Hammergren C. Data Warehousing For Dummies Новинка

Thomas Hammergren C. Data Warehousing For Dummies

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Data warehousing is one of the hottest business topics, and there’s more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition. Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to: Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic!
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

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
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

3769.72 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
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...
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

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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.
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

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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.
Antonios Chorianopoulos Effective CRM using Predictive Analytics Antonios Chorianopoulos Effective CRM using Predictive Analytics Новинка

Antonios Chorianopoulos Effective CRM using Predictive Analytics

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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.
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

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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.
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

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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.
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.
Adam Fowler NoSQL For Dummies Adam Fowler NoSQL For Dummies Новинка

Adam Fowler NoSQL For Dummies

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Get up to speed on the nuances of NoSQL databases and what they mean for your organization This easy to read guide to NoSQL databases provides the type of no-nonsense overview and analysis that you need to learn, including what NoSQL is and which database is right for you. Featuring specific evaluation criteria for NoSQL databases, along with a look into the pros and cons of the most popular options, NoSQL For Dummies provides the fastest and easiest way to dive into the details of this incredible technology. You'll gain an understanding of how to use NoSQL databases for mission-critical enterprise architectures and projects, and real-world examples reinforce the primary points to create an action-oriented resource for IT pros. If you're planning a big data project or platform, you probably already know you need to select a NoSQL database to complete your architecture. But with options flooding the market and updates and add-ons coming at a rapid pace, determining what you require now, and in the future, can be a tall task. This is where NoSQL For Dummies comes in! Learn the basic tenets of NoSQL databases and why they have come to the forefront as data has outpaced the capabilities of relational databases Discover major players among NoSQL databases, including Cassandra, MongoDB, MarkLogic, Neo4J, and others Get an in-depth look at the benefits and disadvantages of the wide variety of NoSQL database options Explore the needs of your organization as they relate to the capabilities of specific NoSQL databases Big data and Hadoop get all the attention, but when it comes down to it, NoSQL databases are the engines that power many big data analytics initiatives. With NoSQL For Dummies, you'll go beyond relational databases to ramp up your enterprise's data architecture in no time.
Meta Brown S. Data Mining For Dummies Meta Brown S. Data Mining For Dummies Новинка

Meta Brown S. Data Mining For Dummies

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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.
Gordon Linoff S. Data Analysis Using SQL and Excel Gordon Linoff S. Data Analysis Using SQL and Excel Новинка

Gordon Linoff S. Data Analysis Using SQL and Excel

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A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the «where» and «why» of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. Understand core analytic techniques that work with SQL and Excel Ensure your analytic approach gets you the results you need Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.
Ken Cook Access 2019 For Dummies Ken Cook Access 2019 For Dummies Новинка

Ken Cook Access 2019 For Dummies

Easy steps to practical databases People who really know how to build, populate, and simplify databases are few and far between. Access 2019 For Dummies is here to help you join the ranks of office heroes who possess these precious skills. This book offers clear and simple advice on how to build and operate databases as well as create simple forms, import data from outside sources, query databases for information, and share knowledge in reports. In short, it’s the book that holds all the secrets behind the mysteries of Access! Build effective databases from the ground up Simplify your data entry with forms and tables Write queries that produce answers to your data questions Simplify input with forms There’s no time like the present to get your hands on the insight that database beginners need to become Access gurus.
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

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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.
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

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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.
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

7384.87 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.
Bhattrai Premlal Membership Card Generation Based on Clustering and Optimization Models Bhattrai Premlal Membership Card Generation Based on Clustering and Optimization Models Новинка

Bhattrai Premlal Membership Card Generation Based on Clustering and Optimization Models

The research aimed to develop a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical optimization in a hypermarket.Data mining as a technique is used to find interesting and valuable knowledge from huge amount of stored data within databases. We employed hierarchical and fuzzy clustering method in data selection preprocessing step for customer segmentation. Further, a methodological approach for clustering of customers based on their characteristics in order to define membership cards based on mathematical optimization is devised in this research. This study provides a basis for generating customer membership cards in a hypermarket by way of data mining techniques. Fuzzy clustering method helps to cluster similar customer information into groups. The resulting clusters are then used for optimization model in order to generate membership cards.
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

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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
Hendrik Eisenberg Statistische Methoden des Data Mining und deren Anwendung Hendrik Eisenberg Statistische Methoden des Data Mining und deren Anwendung Новинка

Hendrik Eisenberg Statistische Methoden des Data Mining und deren Anwendung

Inhaltsangabe:Zusammenfassung: In dieser Arbeit stehen neben dem Begriff des „Data Mining“ besonders die statistischen Methoden im Mittelpunkt. Interessenten sollen den kreativen Prozess des Data Mining näher kennen lernen und erfahren, welche Rolle dabei der Statistik zukommt. Das Ziel der Arbeit ist, eine weiterreichende Darstellung des Prozesses des Data Mining mit statistischen Methoden zu erstellen, angefangen bei der Zielfindung, über die Modellbildung, bis hin zur Bewertung der Ergebnisse. Dabei orientiert sich die Vorgehensweise der systematischen Auswertung an der Methode des CRoss Industry Standard Process for Data Mining, mit der sich Data Mining Prozesse beschreiben lassen. Zum besseren Verständnis werden grundlegende Begriffe zum Data Mining sowie die bedeutsamsten Methoden und Verfahren zur statistischen Datenanalyse erläutert, welche bei den im Anschluss aufgezeigten Data Mining Problemen zur Anwendung kommen. Die veranschaulichten Analyseprobleme entsprechen den Aufgaben der Data Mining Cups der Jahre 2001 und 2002. Dabei werden die zur Lösung angewendeten statistischen Methoden nachvollziehbar wiedergegeben und es wird auf die kritischen Erfolgsfaktoren eingegangen. Oftmals wirken sich schon einzelne Teilentscheidungen bei der Datenaufbereitung und bei den eingesetzten Klassifizierungsmethoden auf die Lösung der Data Mining Aufgabe aus. Daher stellte sich die Frage, wie solche Abweichungen von den aufgezeigten Methoden aussehen könnten. In dieser Arbeit werde...
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.
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

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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.
Bater Makhabel Learning Data Mining with R Bater Makhabel Learning Data Mining with R Новинка

Bater Makhabel Learning Data Mining with R

Книга "Learning Data Mining with R".
Hengqing Tong Developing Econometrics Hengqing Tong Developing Econometrics Новинка

Hengqing Tong Developing Econometrics

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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.
Simon Munzert Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining Simon Munzert Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining Новинка

Simon Munzert Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining

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A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.
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...
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

5880.76 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.
Tim Rey, Arthur Kordon, Chip Wells Applied Data Mining for Forecasting Using SAS Tim Rey, Arthur Kordon, Chip Wells Applied Data Mining for Forecasting Using SAS Новинка

Tim Rey, Arthur Kordon, Chip Wells Applied Data Mining for Forecasting Using SAS

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable.
Ken Cook Access 2016 For Dummies Ken Cook Access 2016 For Dummies Новинка

Ken Cook Access 2016 For Dummies

1884.23 руб. или Купить в рассрочку!
Your all-access guide to all things Access 2016 If you don't know a relational database from an isolationist table—but still need to figure out how to organize and analyze your data—Access 2016 For Dummies is for you. Written in a friendly and accessible manner, it assumes no prior Access or database-building knowledge and walks you through the basics of creating tables to store your data, building forms that ease data entry, writing queries that pull real information from your data, and creating reports that back up your analysis. Add in a dash of humor and fun, and Access 2016 For Dummies is the only resource you'll need to go from data rookie to data pro! This expanded and updated edition of Access For Dummies covers all of the latest information and features to help data newcomers better understand Access' role in the world of data analysis and data science. Inside, you'll get a crash course on how databases work—and how to build one from the ground up. Plus, you'll find step-by-step guidance on how to structure data to make it useful, manipulate, edit, and import data into your database, write and execute queries to gain insight from your data, and report data in elegant ways. Speak the lingo of database builders and create databases that suit your needs Organize your data into tables and build forms that ease data entry Query your data to get answers right Create reports that tell the story of your data findings If you have little to no experience with creating and managing a database of any sort, Access 2016 For Dummies is the perfect starting point for learning the basics of building databases, simplifying data entry and reporting, and improving your overall data skills.
И. И. Холод Технологии анализа данных: Data Mining, Visual Mining, Text Mining, OLAP И. И. Холод Технологии анализа данных: Data Mining, Visual Mining, Text Mining, OLAP Новинка

И. И. Холод Технологии анализа данных: Data Mining, Visual Mining, Text Mining, OLAP

Книга является вторым, обновленным и дополненным, изданием учебного пособия «Методы и модели анализа данных: OLAP и Data Mining». Излагаются основные направления в области разработки корпоративных систем: организация хранилищ данных, распределенный, оперативный (OLAP), интеллектуальный (Data Mining), визуальный (Visual Mining) и текстовый (Text Mining) анализ данных. Приведено описание методов и алгоритмов решения основных задач анализа: классификации, кластеризации и др. Описание идеи каждого метода дополняется конкретным примером его применения. Для студентов и специалистов в области анализа данных.
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.
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

10047.35 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.
Martin Kneip Data Mining Martin Kneip Data Mining Новинка

Martin Kneip Data Mining

Studienarbeit aus dem Jahr 2005 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 2,0, FernUniversität Hagen (Wirtschaftswissenschaften), 129 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: In der heutigen Zeit werden Unternehmen und Institutionen, bedingt durch den technologischen Fortschritt, mit einer enormen Flut unterschiedlichster Daten konfrontiert. Das Earth Observing System der NASA mit seinen Satelliten produziert beispielsweise über 50GB Daten pro Stunde. Insbesondere für das Management enthalten diese Daten wertvolles Wissen, um Probleme aufzudecken, Produktionsabläufe zu optimieren oder bessere Zukunftsprognosen anzustellen. Resultat dieser Bemühungen um den strategischen Wettbewerbsfaktor Wissen ist eine langfristig bessere Positionierung des Unternehmens am Markt.Ohne Analyse dieser Daten steht jedoch das Wissen nicht zur Verfügung. Aufgrund der Datenmenge scheiden jedoch manuelle Analyseverfahren aus und es werden schnelle und effiziente automatisierte Analyseverfahren nötig. Mit dem Data Mining beziehungsweise dem Knowledge Discovery in Databases (KDD) existiert ein mächtiges Werkzeug, um die sehr umfangreiche Aufgabe der Wissensextraktion zu bewältigen, so daß das Interesse der Forschung und Industrie an diesem Gebiet stetig ansteigt.Anzumerken ist jedoch, daß das Data Mining ein relativ junges Forschungsgebiet ist und daher die Meinungen, was Data Mining ist und was Data Mining zugeordnet werden soll, teilweise stark differieren.In diese...
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.
Ahlemeyer-Stubbe Andrea A Practical Guide to Data Mining for Business and Industry Ahlemeyer-Stubbe Andrea A Practical Guide to Data Mining for Business and Industry Новинка

Ahlemeyer-Stubbe Andrea A Practical Guide to Data Mining for Business and Industry

6257.73 руб. или Купить в рассрочку!
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Darius Dziuda M. Data Mining for Genomics and Proteomics. Analysis of Gene and Protein Expression Data Darius Dziuda M. Data Mining for Genomics and Proteomics. Analysis of Gene and Protein Expression Data Новинка

Darius Dziuda M. Data Mining for Genomics and Proteomics. Analysis of Gene and Protein Expression Data

8289.61 руб. или Купить в рассрочку!
Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.
Yuichi Motai Data-Variant Kernel Analysis Yuichi Motai Data-Variant Kernel Analysis Новинка

Yuichi Motai Data-Variant Kernel Analysis

9675.25 руб. или Купить в рассрочку!
Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
Debarchan Sarkar Mastering SQL Server 2014 Data Mining Debarchan Sarkar Mastering SQL Server 2014 Data Mining Новинка

Debarchan Sarkar Mastering SQL Server 2014 Data Mining

Книга "Mastering SQL Server 2014 Data Mining".
Olivier Pivert NoSQL Data Models. Trends and Challenges Olivier Pivert NoSQL Data Models. Trends and Challenges Новинка

Olivier Pivert NoSQL Data Models. Trends and Challenges

9110.15 руб. или Купить в рассрочку!
The topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. The chapters in this book report on research about the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues.
Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with XLMiner Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with XLMiner Новинка

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

10047.35 руб. или Купить в рассрочку!
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition «…full of vivid and thought-provoking anecdotes… needs to be read by anyone with a serious interest in research and marketing.»– Research Magazine «Shmueli et al. have done a wonderful job in presenting the field of data mining – a welcome addition to the literature.» – ComputingReviews.com «Excellent choice for business analysts…The book is a perfect fit for its intended audience.» – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization 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, The 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. 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, 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.
Rohan Ahmed Fraud Detection in White-Collar Crime Rohan Ahmed Fraud Detection in White-Collar Crime Новинка

Rohan Ahmed Fraud Detection in White-Collar Crime

Bachelor Thesis from the year 2017 in the subject Computer Science - Commercial Information Technology, grade: 1.3, Heilbronn University, language: English, abstract: White-collar crime is and has always been an urgent issue for the society. In recent years, white-collar crime has increased dramatically by technological advances. The studies show that companies are affected annually by corruption, balance-sheet manipulation, embezzlement, criminal insolvency and other economic crimes. The companies are usually unable to identify the damage caused by fraudulent activities. To prevent fraud, companies have the opportunity to use intelligent IT approaches. The data analyst or the investigator can use the data which is stored digitally in today's world to detect fraud. In the age of Big Data, digital information is increasing enormously. Storage is cheap today and no longer a limited medium. The estimates assume that today up to 80 percent of all operational information is stored in the form of unstructured text documents. This bachelor thesis examines Data Mining and Text Mining as intelligent IT approaches for fraud detection in white-collar crime. Text Mining is related to Data Mining. For a differentiation, the source of the information and the structure is important. Text Mining is mainly concerned with weak- or unstructured data, while Data Mining often relies on structured sources. At the beginning of this bachelor thesis, an insight is first given on white-collar crim...
Daniel Larose T. Data Mining and Predictive Analytics Daniel Larose T. Data Mining and Predictive Analytics Новинка

Daniel Larose T. Data Mining and Predictive Analytics

10418.74 руб. или Купить в рассрочку!
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Holger Herrmann Techniken des Data Mining, Knowledge Discovery und SPSS Holger Herrmann Techniken des Data Mining, Knowledge Discovery und SPSS Новинка

Holger Herrmann Techniken des Data Mining, Knowledge Discovery und SPSS

Studienarbeit aus dem Jahr 2004 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,7, FernUniversität Hagen, Veranstaltung: Wirtschaftsinformatik, 7 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: 1 Einleitung 1.1 Definition und Ziele des Data Mining 1.2 Vorgehensweise 1.3 Probleme beim Data Mining 1.4 Aufbereitung der Daten 2 Data Mining und Knowledge Discovery 2.1 Definition: „Knowledge Discovery" 2.2 Die Phasen des Knowledge Discovery 3 Data Mining als Bestandteil des Data Warehousing 4 Techniken des Data Mining 4.1 Klassifikation und Entscheidungsbaum 4.1.1 Ziele der Verfahren 4.1.2 Beschreibung der Klassifikation und des Entscheidungsbaums 4.1.3 Anwendungsbeispiel eines Entscheidungsbaums 4.1.4 Probleme bei Klassifikation und Entscheidungsbaum 4.2 Die Assoziationsregeln 4.2.1 Ziel der Assoziationsregeln 4.2.2 Erzeugung von Assoziationsregeln 4.2.3 Anwendungsbeispiel von Assoziationsregeln 4.2.4 Probleme bei Assoziationsregeln 4.3 Das Clustering 4.3.1 Ziel des Clustering 4.3.2 Beschreibung des Clustering 4.3.3 Anwendungsbeispiel des Clustering 4.3.4 Probleme beim Clustering 5 Data Mining mit SPSS 5.1 Was ist SPSS? 5.2 Ein Beispiel zur Entscheidungsbaum-Analyse 6 Data Mining mit SAS 6.1 Das Unternehmen „SAS" 6.2 Die Lösungen von SAS 6.3 Die Brücke zwischen Theorie und Praxis: Musterbeispiel KSFE 7 Zusammenfassung und Ausblick
Yan Zhao Interactive Data Mining Yan Zhao Interactive Data Mining Новинка

Yan Zhao Interactive Data Mining

Yan Zhao's book, entitled "Interactive Data Mining" provides a con­ceptual framework and a systematic study of human-computer inter­actions and collaborations for effective data mining. The thesis is based on an assumption that the effectiveness of data mining systems depends crucially on semantics information about data and different user requirements. In contrast to many data mining models that con­centrate on automation and efficiency, interactive data mining sys­tems focus on adaptive and effective communication between human users and computer systems. Interactive systems fully explore the power of human intuition, creativity, heuristics and strategies with supports from computers. The thesis is well-balanced between theo­retical investigation and experimental evaluations. A user-oriented three-layered conceptual model is proposed. Within the framework, user perceptions and requirements are studied formally at the philo­sophical, technique and the application layers. The separation of the three layers leads to many new insights into data mining. Based on the conceptual framework, a prototype of Interactive Classification System (ICS) has been implemented.
Andre Hiller Einfuhrung in den Einsatz von Data Mining Andre Hiller Einfuhrung in den Einsatz von Data Mining Новинка

Andre Hiller Einfuhrung in den Einsatz von Data Mining

Diplomarbeit aus dem Jahr 2003 im Fachbereich Statistik, Note: 1,3, Hochschule Anhalt - Standort Bernburg, 16 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Diese Arbeit soll eine Einführung in den Einsatz von Data Mining bei der Störungsbeseitigung geben. Als Testobjekt wurde die Störungsdatenbank der SOLVAY Deutschland GmbH in Bernburg gewählt. Es soll geprüft werden, ob diese die Voraussetzungen für Data Mining - Analysen erfüllt und welche Ergebnisse erzielt werden können. Des Weiteren soll ein Ausblick auf weitere Möglichkeiten des Data Mining - Einsatzes gegeben werden. Das Interesse an Data Mining wurde durch ständig auftauchende Berichte, abgehaltene Konferenzen und Wettbewerbe geweckt. Wie z.B. durch den Artikel von Michael Gonzales, dem Geschäftsführer der „Focus Group, Ltd „ einer Unternehmensberatung, die sich auf Data Mining spezialisiert hat. In seinem Artikel beschreibt er die Notwendigkeit der Nutzung von Data Mining für die Bewältigung der Informationsflut und den daraus entstehenden Informationsvorsprung gegenüber anderen Unternehmen. Seiner Meinung ist die Bereitstellung der Daten durch ein Data Warehouse die wichtigste Voraussetzung für die Durchführung von Data Mining - Projekten, da 80% der benötigten Zeit für die Datenaufbereitung benötigt werden. Durch die richtige Vorbereitung der Daten können schneller Ergebnisse erzielt werden und diese für die Entscheidungsfindung genutzt werden.
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

7761.85 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

9675.25 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.
Contemporary Perspectives in Data Mining Contemporary Perspectives in Data Mining Новинка

Contemporary Perspectives in Data Mining

The series, Contemporary Perspectives on Data Mining, is composed of blindrefereed scholarly research methods and applications of data mining. This serieswill be targeted both at the academic community, as well as the businesspractitioner.Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematicaltechniques. The knowledge is extracted form this data by examining the patterns of the data, whether they beassociations of groups or things, predictions, sequential relationships between time order events or naturalgroups.Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships,retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and lawenforcement.
Mirko Prescha Data-Mining im Immobilien-Business Mirko Prescha Data-Mining im Immobilien-Business Новинка

Mirko Prescha Data-Mining im Immobilien-Business

In diesem Werk werden Konzepte erarbeitet, die zeigen, wie sich Data Mining zur Unterstützung von Marketing und Customer Relationship Management im Immobilien E-Business praktisch nutzen lässt. Dazu werden die prinzipiellen Verfahren und Effekte des Data Mining erörtert und anschließend erarbeitet, wie sie sich im genannten praktischen Umfeld zulässigerweise effektiv nutzbar machen lassen.
И. И. Холод Анализ данных и процессов И. И. Холод Анализ данных и процессов Новинка

И. И. Холод Анализ данных и процессов

Излагаются основные направления в области разработки корпоративных систем: организация хранилищ данных, оперативный (OLAP) и интеллектуальный (Data Mining) анализ данных. В третьем издании по сравнению со вторым, выходившем под названием «Технологии анализа данных: Data Mining, Text Mining, Visual Mining, OLAP», добавлены визуальный (Visual Mining) и текстовый (Text Mining) анализ данных, анализ процессов (Process Mining), анализ Web-ресурсов (Web mining) и анализ в режиме реального времени (Real-Time Data Mining). Приведено описание методов и алгоритмов решения основных задач анализа: классификации, кластеризации и др. Описание идеи каждого метода дополняется конкретным примером его использования. Для студентов, инженеров и специалистов в области анализа данных и процессов. (Компакт-диск прилагается только к печатному изданию.)
Alan Anderson Statistics for Big Data For Dummies Alan Anderson Statistics for Big Data For Dummies Новинка

Alan Anderson Statistics for Big Data For Dummies

1444.42 руб. или Купить в рассрочку!
The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word «statistics» alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.
Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications in R Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications in R Новинка

Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications in R

10047.35 руб. или Купить в рассрочку!
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students • More than a dozen case studies demonstrating applications for the data mining techniques described • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R 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 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at 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 (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American St
Hugo Kubinyi Data Mining in Drug Discovery Hugo Kubinyi Data Mining in Drug Discovery Новинка

Hugo Kubinyi Data Mining in Drug Discovery

14586.66 руб. или Купить в рассрочку!
Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.
Reyländer Sabine Scientific databases for gait analyses Reyländer Sabine Scientific databases for gait analyses Новинка

Reyländer Sabine Scientific databases for gait analyses

The use of database for different kind of data is nowadays common. Especially in sectors, which work with a huge amount of data, databases are used. Such an example is a gait analysis laboratory. During one year a huge amount of data will be collected. Based on the amount and high quality of the data, they suits perfectly for a meaningful study. To enable such a scientific analysis, based on different scientific issues, the information has to be brought together and stored at one single place. This central storage of the data makes scientific studies time-saving and costs-saving. Based on the during this thesis developed concept of a scientific database and WIDAGA it is possible to draw samples from gait analysis data for different scientific analysis easily. The amount of data for scientific use can be extended easily be cooperation of different gait analysis laboratories. But in this case the data protection act has to be minded. Especially the rules for data sharing and data exchange have to be noted. Based on functionality of WIDAGA the economic efficiency of scientific studies in the gait analysis sector can be increased.
Malcolm Atkinson The Data Bonanza. Improving Knowledge Discovery in Science, Engineering, and Business Malcolm Atkinson The Data Bonanza. Improving Knowledge Discovery in Science, Engineering, and Business Новинка

Malcolm Atkinson The Data Bonanza. Improving Knowledge Discovery in Science, Engineering, and Business

8484.11 руб. или Купить в рассрочку!
Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.
Albalate Amparo Semi-Supervised and Unsupervised Machine Learning. Novel Strategies Albalate Amparo Semi-Supervised and Unsupervised Machine Learning. Novel Strategies Новинка

Albalate Amparo Semi-Supervised and Unsupervised Machine Learning. Novel Strategies

8519.56 руб. или Купить в рассрочку!
This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.
Contemporary Perspectives in Data Mining, Volume 2 Contemporary Perspectives in Data Mining, Volume 2 Новинка

Contemporary Perspectives in Data Mining, Volume 2

A volume in Contemporary Perspectives in Data MiningSeries Editors Kenneth D. Lawrence, New Jersey Institute of Technologyand Ronald K. Klimberg, Saint Joseph's UniversityThe series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarlyresearch methods and applications of data mining. This series will be targeted both at theacademic community, as well as the business practitioner.Data mining seeks to discover knowledge from vast amounts of data with the use of statisticaland mathematical techniques. The knowledge is extracted from this data by examining thepatterns of the data, whether they be associations of groups or things, predictions, sequentialrelationships between time order events or natural groups.Data mining applications are in marketing (customer loyalty, identifying profitable customers, instorepromotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderateasset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in amobility network and road safety modeling.)
Yasin Yakut Erzielen von Wettbewerbsvorteilen durch Data Mining in Produktion und Logistik Yasin Yakut Erzielen von Wettbewerbsvorteilen durch Data Mining in Produktion und Logistik Новинка

Yasin Yakut Erzielen von Wettbewerbsvorteilen durch Data Mining in Produktion und Logistik

Diese Studie widmet sich dem Thema „Anwendungsfelder für Data Mining in Produktion und Logistik". Data Mining Verfahren sind in der Praxis weit verbreitet und unterstützen mit wertvollem Wissen Geschäftsführungen bei Ihren Entscheidungen. Vor allem aus den Bereichen Vertrieb und Marketing ist Data Mining nicht mehr wegzudenken. Anhand von vorhandenen Daten können z.B. Informationen generiert werden, um verfeinerte Werbestrategien zu bilden, die umsatzfördernd sind. Da aber auch in der Produktion und Logistik durch die zunehmende Automatisierung Unmengen an Daten anfallen, ergibt sich hier ebenfalls die Möglichkeit diese nach Optimierungspotential zu untersuchen. In diesem Zusammenhang soll diese Studie bei der Aufdeckung neuer Einsatzmöglichkeiten mitwirken.Einige Unternehmen setzen mittlerweile Data Mining Methoden effizient in der Produktions- bzw. Logistikkette ein und profitieren von den Vorteilen: Kostspielige Reparaturen werden durch präventive Wartungen vermieden;Fehler werden schneller diagnostiziert und gezielt beseitigt; Lagerkosten können mit den geeigneten Erkenntnissen gesenkt werden.All das verborgene Wissen in den Datenbergen könnte Unternehmen durch den Einsatz von Data Mining Methoden viel Geld, Zeit und vor allem Nerven ersparen. In dieser Untersuchung wird zudem aufbauend auf den Erkenntnissen einer Online-Befragung, der Einsatz von Data Mining in einem der potentiellen Anwendungsbereiche beispielhaft umgesetzt und hinsichtlich der erzielbaren Potential...
Harvey Goldstein Methodological Developments in Data Linkage Harvey Goldstein Methodological Developments in Data Linkage Новинка

Harvey Goldstein Methodological Developments in Data Linkage

A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.
T. Brüggemann, P. Hedström, M. Josefsson Data mining and data based direct marketing activities T. Brüggemann, P. Hedström, M. Josefsson Data mining and data based direct marketing activities Новинка

T. Brüggemann, P. Hedström, M. Josefsson Data mining and data based direct marketing activities

Master's Thesis from the year 2004 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, grade: 1,7 (A-), Växjö University (School of Management and Economics), course: International Business Environment, 40 entries in the bibliography, language: English, abstract: Widespread changes within business environments in recent years has demanded acquisitions of new tools that are more skilled to cope with new challenges and demands in business. Advances in computer technologies, higher accessibility of computer associated tools and decreased prices of general computer-related products are reasons enough for at least considerations about higher usage of new technologies. Particularly in direct marketing activities discussed technology is called Data Mining.Companies are faced with hosts of data collected in their data repositories. Of course, companies want to make use of their data and aim to discover interesting patterns of knowledge within their data repositories. Direct marketers which can be involved in catalogue marketing, telemarketing or widely known direct-mail marketing are intensive users of Data Mining Technologies. Because of that, the authors strive to do research concerning reasons for and advantages and disadvantages with using Data Mining as support for direct marketing activities.Chapter 1 deals with general information for the reader as support for delving into the topic. The included problem discussion fi...

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Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students • More than a dozen case studies demonstrating applications for the data mining techniques described • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R 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 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at 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 (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American St
Продажа data mining for multimedia databases лучших цены всего мира
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