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Informationen zum Autor GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management. Klappentext The newest edition of the leading introductory book on data mining, fully updated and revised Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business. In this latest edition, Linoff and Berry have made extensive updates and revisions to every chapter and added several new ones. The book retains the focus of earlier editions?showing marketing analysts, business managers, and data mining specialists how to harness data mining methods and techniques to solve important business problems. While never sacrificing accuracy for the sake of simplicity, Linoff and Berry present even complex topics in clear, concise English with minimal use of technical jargon or mathematical formulas. Technical topics are illustrated with case studies and practical real-world examples drawn from the authors' experiences, and every chapter contains valuable tips for practitioners. Among the techniques newly covered, or covered in greater depth, are linear and logistic regression models, incremental response (uplift) modeling, naïve Bayesian models, table lookup models, similarity models, radial basis function networks, expectation maximization (EM) clustering, and swarm intelligence. New chapters are devoted to data preparation, derived variables, principal components and other variable reduction techniques, and text mining. After establishing the business context with an overview of data mining applications, and introducing aspects of data mining methodology common to all data mining projects, the book covers each important data mining technique in detail. This third edition of Data Mining Techniques covers such topics as: How to create stable, long-lasting predictive models Data preparation and variable selection Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning Finding patterns with undirected techniques such as clustering, association rules, and link analysis Modeling business time-to-event problems such as time to next purchase and expected remaining lifetime Mining unstructured text The companion website provides data that can be used to test out the various data mining techniques in the book. Zusammenfassung 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. Inhaltsverzeichnis Introduction xxxvii Chapter 1 What Is Data Mining and Why Do It? 1 What Is Data Mining? 2 Data Mining Is a Business Process 2 Large Amounts of Data 3 Meaningful Patterns and Rules 3 Data Mining and Customer Relationship Management 4 Why Now? 6 Data Is Being Produced 6 Data Is Being Warehoused 6 Computing Power Is Affordable 7 Interest in Customer Relationship Management Is Strong 7 Commercial Data Mining Software ...
Sommario
Introduction.
Chapter 1 What Is Data Mining and Why Do It?
Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management.
Chapter 3 The Data Mining Process.
Chapter 4 Statistics 101: What You Should Know About Data.
Chapter 5 Descriptions and Prediction: Profiling and Predictive Modeling.
Chapter 6 Data Mining Using Classic Statistical Techniques.
Chapter 7 Decision Trees.
Chapter 8 Artifi cial Neural Networks.
Chapter 9 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering.
Chapter 10 Knowing When to Worry: Using Survival Analysis to Understand Customers.
Chapter 11 Genetic Algorithms and Swarm Intelligence.
Chapter 12 Tell Me Something New: Pattern Discovery and Data Mining.
Chapter 13 Finding Islands of Similarity: Automatic Cluster Detection.
Chapter 14 Alternative Approaches to Cluster Detection.
Chapter 15 Market Basket Analysis and Association Rules.
Chapter 16 Link Analysis.
Chapter 17 Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining.
Chapter 18 Building Customer Signatures.
Chapter 19 Derived Variables: Making the Data Mean More.
Chapter 20 Too Much of a Good Thing? Techniques for Reducing the Number of Variables.
Chapter 21 Listen Carefully to What Your Customers Say: Text Mining.
Index.
