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Data Mining Techniques


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Table of Contents

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.

About the Author

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.

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