Data Mining Techniques For Marketing, Sales, and Customer Relationship Management

ISBN-10: 0471470643

ISBN-13: 9780471470649

Edition: 2nd 2004 (Revised)

Authors: Michael J. A. Berry, Gordon S. Linoff

List price: $50.00 Buy it from $3.00
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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
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Book details

List price: $50.00
Edition: 2nd
Copyright year: 2004
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/9/2004
Binding: Paperback
Pages: 672
Size: 9.25" wide x 7.25" long x 1.00" tall
Weight: 2.2
Language: English

About the Authors
Why and What Is Data Mining?
The Virtuous Cycle of Data Mining
Data Mining Methodology and Best Practices
Data Mining Applications in Marketing and Customer Relationship Management
The Lure of Statistics: Data Mining Using Familiar Tools
Decision Trees
Artificial Neural Networks
Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
Market Basket Analysis and Association Rules
Link Analysis
Automatic Cluster Detection
Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing
Genetic Algorithms
Data Mining throughout the Customer Life Cycle
Data Warehousing, OLAP, and Data Mining
Building the Data Mining Environment
Preparing Data for Mining
Putting Data Mining to Work
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