introduction to data mining data warehousing



Khusboo Saxena, Sandeep Saxena, Akash Saxena DATA MINING AND WAREHOUSING Khusboo Saxena, Sandeep Saxena, Akash Saxena DATA MINING AND WAREHOUSING Новинка

Khusboo Saxena, Sandeep Saxena, Akash Saxena DATA MINING AND WAREHOUSING

2377 руб.
Description:The book has been written in such a way that the concepts are explained in detail, giving adequate emphasis on examples. To make clarity on the topic, diagrams are given extensively throughout the text. The book discusses design issues for phases of mining in substantial depth. The stress is more on problem solving.Various Comprehensive coverage of various aspects of Data Mining and Warehousing conceptsStrictly in accordance for the syllabus covered under B.E./B.Tech/MCASimple language, crystal clear approach, straight forward comprehensible presentationAdopting user friendly classroom lecture styleThe concepts are duly supported by sever examplesSyllabus coverage of three universities UPTU, RTU and RGPVTable Of Contents:Chapter 1 : Introduction To Data MiningChapter 2 : Concept DescriptionChapter 3 : Association Rule MiningChapter 4 : Classification and PredictionsChapter 5 : Cluster AnalysisChapter 6 : Introduction to Data WarehouseChapter 7 : OLAP TechnologyChapter 8 : Advance Topic On Data Mining and Warehousing
Thomas Hammergren C. Data Warehousing For Dummies Thomas Hammergren C. Data Warehousing For Dummies Новинка

Thomas Hammergren C. Data Warehousing For Dummies

2311.19 руб.
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!
Paulraj Ponniah Data Warehousing Fundamentals for IT Professionals Paulraj Ponniah Data Warehousing Fundamentals for IT Professionals Новинка

Paulraj Ponniah Data Warehousing Fundamentals for IT Professionals

11642.06 руб.
Cutting-edge content and guidance from a data warehousing expert—now expanded to reflect field trends Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery. This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information. Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
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

3959.89 руб.
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
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

14651.58 руб.
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.
Christian Coté, Michelle Gutzait Hands-On Data Warehousing with Azure Data Factory Christian Coté, Michelle Gutzait Hands-On Data Warehousing with Azure Data Factory Новинка

Christian Coté, Michelle Gutzait Hands-On Data Warehousing with Azure Data Factory

6127 руб.
Книга "Hands-On Data Warehousing with Azure Data Factory".
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

7361.43 руб.
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
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

2112.3 руб.
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.
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

4752 руб.
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...
Kelly Data Warehousing Updated & Exp Kelly Data Warehousing Updated & Exp Новинка

Kelly Data Warehousing Updated & Exp

9152 руб.
Книга "Data Warehousing Updated & Exp".
Carlo Vercellis Business Intelligence. Data Mining and Optimization for Decision Making Carlo Vercellis Business Intelligence. Data Mining and Optimization for Decision Making Новинка

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

14651.58 руб.
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
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

10755.4 руб.
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.
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

14366.28 руб.
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.
Soumya Sen, Agostino Cortesi, Nabendu Chaki Hyper-lattice Algebraic Model for Data Warehousing Soumya Sen, Agostino Cortesi, Nabendu Chaki Hyper-lattice Algebraic Model for Data Warehousing Новинка

Soumya Sen, Agostino Cortesi, Nabendu Chaki Hyper-lattice Algebraic Model for Data Warehousing

9464 руб.
Книга "Hyper-lattice Algebraic Model for Data Warehousing".
Hitesh Chhinkaniwala and Sanjay Garg Privacy Preserving Data Mining - Issues & Techniques Hitesh Chhinkaniwala and Sanjay Garg Privacy Preserving Data Mining - Issues & Techniques Новинка

Hitesh Chhinkaniwala and Sanjay Garg Privacy Preserving Data Mining - Issues & Techniques

4755 руб.
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
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

1789 руб.
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
Meta Brown S. Data Mining For Dummies Meta Brown S. Data Mining For Dummies Новинка

Meta Brown S. Data Mining For Dummies

2311.19 руб.
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.
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

7552 руб.
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.
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

3302.64 руб.
The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Russell Anderson K. Visual Data Mining. The VisMiner Approach Russell Anderson K. Visual Data Mining. The VisMiner Approach Новинка

Russell Anderson K. Visual Data Mining. The VisMiner Approach

6652.61 руб.
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
Stephan Kudyba, Richard Hoptroff Data Mining and Business Intelligence. A Guide to Productivity Stephan Kudyba, Richard Hoptroff Data Mining and Business Intelligence. A Guide to Productivity Новинка

Stephan Kudyba, Richard Hoptroff Data Mining and Business Intelligence. A Guide to Productivity

6927 руб.
Книга "Data Mining and Business Intelligence. A Guide to Productivity".
Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery Новинка

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

8834.79 руб.
A comprehensive introduction to statistical methods for data mining and knowledge discovery. Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples 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. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.
Andre Carvalho A General Introduction to Data Analytics Andre Carvalho A General Introduction to Data Analytics Новинка

Andre Carvalho A General Introduction to Data Analytics

6596.51 руб.
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
Megan Squire Mastering Data Mining with Python - Find patterns hidden in your data Megan Squire Mastering Data Mining with Python - Find patterns hidden in your data Новинка

Megan Squire Mastering Data Mining with Python - Find patterns hidden in your data

6889 руб.
Книга "Mastering Data Mining with Python - Find patterns hidden in your data".
Antonios Chorianopoulos Effective CRM using Predictive Analytics Antonios Chorianopoulos Effective CRM using Predictive Analytics Новинка

Antonios Chorianopoulos Effective CRM using Predictive Analytics

4198.37 руб.
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.
S. Finlay Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods S. Finlay Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods Новинка

S. Finlay Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods

5252 руб.
Книга "Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods".
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

6145.94 руб.
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.
Cox An Introduction to Multivariate Data Cox An Introduction to Multivariate Data Новинка

Cox An Introduction to Multivariate Data

6327 руб.
Книга "An Introduction to Multivariate Data".
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

6177.42 руб.
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.
Tiffany Bergin An Introduction to Data Analysis Tiffany Bergin An Introduction to Data Analysis Новинка

Tiffany Bergin An Introduction to Data Analysis

4939 руб.
Книга "An Introduction to Data Analysis".
Hengqing Tong Developing Econometrics Hengqing Tong Developing Econometrics Новинка

Hengqing Tong Developing Econometrics

9756.83 руб.
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.
Sagarkumar Badhiye,Prashant Chatur and Bhushan Wakode Temperature & Humidity Prediction Using Data Mining Sagarkumar Badhiye,Prashant Chatur and Bhushan Wakode Temperature & Humidity Prediction Using Data Mining Новинка

Sagarkumar Badhiye,Prashant Chatur and Bhushan Wakode Temperature & Humidity Prediction Using Data Mining

3212 руб.
The scope of this research work is to investigate the importance of data mining technique in understanding the variation of temperature and humidity data for which some statistical and mathematical models are used. Here, historical climate data (temperature and humidity) of a region and data mining algorithm K-Nearest Neighbor is used for prediction of temperature and humidity values based on which classification of climate condition is done. It has become important to find an effective and accurate tool to analyze and extract hidden knowledge from climate data due to its increasing availability during the last decade. Knowledge of climate data in a region is essential for business, society, agriculture, energy applications, research and development. Temperature and humidity data is also used in the estimation of bio-meteorological parameters in a region. Data Mining is recently applied to show affect of climate variation on human society and thus, statistical Data Miner software is used in this research work for the data mining purpose which is intelligent data miner software tool where various algorithms are applied.
David L. Olson Data Mining Models, Second Edition David L. Olson Data Mining Models, Second Edition Новинка

David L. Olson Data Mining Models, Second Edition

4814 руб.
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.
Jose Jesus Salcedo Machine Learning for Data Mining Jose Jesus Salcedo Machine Learning for Data Mining Новинка

Jose Jesus Salcedo Machine Learning for Data Mining

4139 руб.
Книга "Machine Learning for Data Mining".
David L. Olson Data Mining Models David L. Olson Data Mining Models Новинка

David L. Olson Data Mining Models

4489 руб.
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.
Jens Meyer Real Time Data Warehousing Jens Meyer Real Time Data Warehousing Новинка

Jens Meyer Real Time Data Warehousing

1627 руб.
Studienarbeit aus dem Jahr 2007 im Fachbereich BWL - Unternehmensforschung, Operations Research, Note: 1,7, Hochschule Hannover, Veranstaltung: Systemtheoretische Entscheidungssysteme, 27 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Das Management jedes Unternehmens steht vor internen sowie externen Rahmenbedingungen zur Entscheidungsfindung. Um die Unternehmensergebnisse optimal Steuern und Planen zu können stehen die Führungskräfte vor vielfältigen Herausforderungen. Für eine Garantie der Zukunftsfähigkeit des Unternehmens, ist es notwendig, sich an diese anzupassen oder aktiv die Rahmenbedingungen mitzugestalten. Hauptziel der vorliegenden Hausarbeit ist die Darstellung, wie die Information von Real Time Data Warehousing, auf die Planung und Steuerung der Unternehmensergebnisse positiv Einfluss nehmen kann.In Kapitel 2 werden die Grundlagen und ein Überblick des Data Warehousing abgebildet. Als Verständnisgrundlage dient die Eingrenzung des Data Warehousing sowie die Entwicklung. Das Kapitel endet mit dem Aufbau des Data Warehousing. Die Erweiterung der Data Warehouse Struktur, wie zum Beispiel die Architektur von Real Time Systemen befindet sich im Kapitel 3. Zur besseren Erörterung erfolgt eine Differenzierung von Real und Right Time Data Warehousing im Abschnitt 3.2. Um einen tieferen Einblick geben zu können findet die Abgrenzung zum klassischen Modell im letzten Abschnitt unter 3.3 statt. Im vierten Abschnitt der Hausarbeit werden praktische Beispiele ...
Andrea Cirillo R Data Mining Andrea Cirillo R Data Mining Новинка

Andrea Cirillo R Data Mining

6089 руб.
Книга "R Data Mining".
Andrew Chisholm Rapidminer for Data Mining Andrew Chisholm Rapidminer for Data Mining Новинка

Andrew Chisholm Rapidminer for Data Mining

5602 руб.
Книга "Rapidminer for Data Mining".
Robert Layton Learning Data Mining with Python Robert Layton Learning Data Mining with Python Новинка

Robert Layton Learning Data Mining with Python

6027 руб.
Книга "Learning Data Mining with Python".
Bater Makhabel Learning Data Mining with R Bater Makhabel Learning Data Mining with R Новинка

Bater Makhabel Learning Data Mining with R

6233 руб.
Книга "Learning Data Mining with R".
Sascha Mendack OLAP without Cubes - Data Analysis in non-Cube Systems Sascha Mendack OLAP without Cubes - Data Analysis in non-Cube Systems Новинка

Sascha Mendack OLAP without Cubes - Data Analysis in non-Cube Systems

8127 руб.
In recent years information has steadily increased in importance for modern day companies and data-warehousing has been described by many as the highest priority post-millennium IT project. As progressive companies are complex and deal with and in fast changing environments, oftentimes with a rapidly growing database, questions regarding current data designs and approaches arise. Can technologies developed in the 1960s support today's and future demands? Author Sascha Mendack gives an introduction to the associative model of data and how it could fulfil the future requirements of modern data-warehousing and reporting. He analyses the data model using the FASMI test by Nigel Pendse and compares its advantages and disadvantages with the well known multidimensional data model. Following up the theoretical review he reviews an OLAP application that uses the new data model. This book is intended for everybody who is interested in an introduction of the associative data model and is looking for new alternatives and approaches in the field of OLAP.
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

3452 руб.
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...
S G GUJAR- TAKALE, S K SHAH DATABASE MANAGEMENT SYSTEM S G GUJAR- TAKALE, S K SHAH DATABASE MANAGEMENT SYSTEM Новинка

S G GUJAR- TAKALE, S K SHAH DATABASE MANAGEMENT SYSTEM

4064 руб.
1 Introduction 2 Data Modelling 3 Relational Model 4 Relational Database Design 5 Introduction to SQl 6 SQL DML Queries 7 Advanced SQL Programming 8 Transaction Management 9 Database Systems Architecture & MONGOD8 10 XML and JSON 11 HADOOP 12 Data Wearhousing And ata Mining 13 Emerging Database Technologies
Discovery Science. 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings Discovery Science. 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings Новинка

Discovery Science. 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings

8977 руб.
This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.
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

12671.64 руб.
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.
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

2102 руб.
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.
Kanhaiya Lal Semantic Web Based Data Cloud Kanhaiya Lal Semantic Web Based Data Cloud Новинка

Kanhaiya Lal Semantic Web Based Data Cloud

8239 руб.
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.
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

9987.88 руб.
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.
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

7757.42 руб.
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.
Philip A. Johnson Introduction to Business Data Communications with Broadband and Wireless Philip A. Johnson Introduction to Business Data Communications with Broadband and Wireless Новинка

Philip A. Johnson Introduction to Business Data Communications with Broadband and Wireless

2027 руб.
Книга "Introduction to Business Data Communications with Broadband and Wireless".
Frank Nielsen Introduction to HPC with MPI for Data Science Frank Nielsen Introduction to HPC with MPI for Data Science Новинка

Frank Nielsen Introduction to HPC with MPI for Data Science

6439 руб.
Книга "Introduction to HPC with MPI for Data Science".
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

9352 руб.
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...
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

8153.41 руб.
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.
Contemporary Perspectives in Data Mining Contemporary Perspectives in Data Mining Новинка

Contemporary Perspectives in Data Mining

6289 руб.
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.
Kwasi Darkwa Ampofo How to Improve Mining Operations with the MHG Mining ERP Kwasi Darkwa Ampofo How to Improve Mining Operations with the MHG Mining ERP Новинка

Kwasi Darkwa Ampofo How to Improve Mining Operations with the MHG Mining ERP

3944 руб.
The American Production and Inventory Control Society investigated that after the successful introduction of an Enterprise Resource Planning (ERP) system a company faces an average of 45% decrease in dispatch delays and 50% decrease in production cycle. Effective access and application of data and information still remains a challenge in most companies. This book therefore assesses how the MHG Mining ERP can improve the mining value chain through remote and real time access to data simply with mobile devices. In conducting the research, data was collected from workers in various mining companies. The analytical method employed was the triangulation of data to ensure the findings made are valid and reliable. The result shows that, MHG Mining ERP has the potential to improve the myriad functions associated with mine operations and other functional areas found in the mining value chain. The ERP as part of its functions convert a typical mobile phone into an autonomous digital data collection device. The findings presented in the book have an implication on the success of operations undertaken by mine managers and supervisors, consultants as well as project management researchers.
Clarisse Dhaenens Metaheuristics for Big Data Clarisse Dhaenens Metaheuristics for Big Data Новинка

Clarisse Dhaenens Metaheuristics for Big Data

8067.28 руб.
Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Penny Jeffrey and Gillian Garbus Mining New Gold-Managing Your Business Data. Data Management for Business Owners Penny Jeffrey and Gillian Garbus Mining New Gold-Managing Your Business Data. Data Management for Business Owners Новинка

Penny Jeffrey and Gillian Garbus Mining New Gold-Managing Your Business Data. Data Management for Business Owners

1602 руб.
Data1. What is the data?2. Can data be validated? Is it accurate?3. How do we store the data?4. Is there a way to make money on the data?5. How does changing expectations of data change your company’s future?In this book, we will be reviewing these issues to help business leaders create a path to protecting, using, and storing data that makes sense and to save money, time, and effort.
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

3959.89 руб.
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.
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

8707.79 руб.
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.
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

5227 руб.
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...

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A comprehensive introduction to statistical methods for data mining and knowledge discovery. Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples 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. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.
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