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Informationen zum Autor Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. He has published several books, including Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Wiley, 2007) and Discovering Knowledge in Data: An Introduction to Data Mining (Wiley, 2005). In addition to his scholarly work, Dr. Larose is a consultant in data mining and statistical analysis working with many high profile clients, including Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc.Chantal D. Larose is a Ph.D. candidate in Statistics at the University of Connecticut. Her research focuses on the imputation of missing data and model-based clustering. She has taught undergraduate statistics since 2011, and is a statistical consultant for DataMiningConsultant.com, LLC. Klappentext Learn methods of data analysis and their application to real-world data setsThis 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:* 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, with exclusive password-protected instructor contentData Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. Zusammenfassung 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. Inhaltsverzeichnis PREFACE xxiACKNOWLEDGMENTS xxixPART I DATA PREPARATION 1CHAPTER 1 AN INTRODUCTION TO DATA MINING AND PREDICTIVE ANALYTICS 3CHAPTER 2 DATA PREPROCESSING 20CHAPTER 3 EXPLORATORY DATA ANALYSIS 54CHAPTER 4 DIMENSION-REDUCTION METHODS 92PART II STATISTICAL ANALYSIS 129CHAPTER 5 UNIVARIATE STATISTICAL ANALYSIS 131CHAPTER 6 MULTIVARIATE STATISTICS 148CHAPTER 7 PREPARING TO MODEL THE DATA 160CHAPTER 8 SIMPLE LINEAR REGRESSION 171CHAPTER 9 MULTIPLE REGRESSION AND MODEL BUILDING 236PART III CLASSIFICATION 299CHAPTER 10 k-NEAREST NEIGHBOR ALGORITHM 301CHAPTER 11 DECISION TREES 317CHAPTER 12 NEURAL NETWORKS 339CHAPTER 13 LOGISTIC REGRESSION 359CHAPTER 14 NAÏVE BAYES AND BAYESIAN NETWORKS 414CHAPTER 15 MODEL EVALUATION TECHNIQUES 451CHAPTER 16 COST-BENEFIT ANALYSIS USING DATA-DRIVEN COSTS 471CHAPTER 17 COST-BENEFIT ANALYSIS FOR TRINARY AND k-NARY CLASSIFICATION MODELS 491CHAPTER 18 GRAPHICAL EVALUATION OF CLASSIFICATION MODELS 510PART IV CLUSTERING 521CHAPTER 19 HIERARCHICAL AND k-MEANS CLUSTERING 523CHAPTER 20 KOHONEN NETWORKS 542CHAPTER 21 BIRCH CLUSTERING 560CHAPTER 22 MEASURING CLUSTER GOODNESS 582PART V ASSOCIATION RULES 601CHAPTER 23 ASSOCIATION RULES 603PART VI ENHANCING MODEL PERFORMANCE 623CHAP...
