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Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management. 3rd Edition

  • ID: 2251741
  • Book
  • April 2011
  • Region: Global
  • 888 Pages
  • John Wiley and Sons Ltd
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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.

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Introduction xxxvii

Chapter 1 What Is Data Mining and Why Do It? 1

Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management 27

Chapter 3 The Data Mining Process 67

Chapter 4 Statistics 101: What You Should Know About Data 101

Chapter 5 Descriptions and Prediction: Profi ling and Predictive Modeling 151

Chapter 6 Data Mining Using Classic Statistical Techniques 195

Chapter 7 Decision Trees 237

Chapter 8 Artifi cial Neural Networks 281

Chapter 9 Nearest Neighbor Approaches: Memory–Based Reasoning and Collaborative Filtering 321

Chapter 10 Knowing When to Worry: Using Survival Analysis to Understand Customers 357

Chapter 11 Genetic Algorithms and Swarm Intelligence 397

Chapter 12 Tell Me Something New: Pattern Discovery and Data Mining 429

Chapter 13 Finding Islands of Similarity: Automatic Cluster Detection 459

Chapter 14 Alternative Approaches to Cluster Detection 499

Chapter 15 Market Basket Analysis and Association Rules 535

Chapter 16 Link Analysis 581

Chapter 17 Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining 613

Chapter 18 Building Customer Signatures 655

Chapter 19 Derived Variables: Making the Data Mean More 693

Chapter 20 Too Much of a Good Thing? Techniques for Reducing the Number of Variables 735

Chapter 21 Listen Carefully to What Your Customers Say: Text Mining 775

Index 821

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Gordon S. Linoff
Michael J. A. Berry
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