Data Mining Techniques
For Marketing, Sales, and Customer Relationship Management
2nd Edition
Published on 14. April 2004
660 pages
978-0-7645-6907-4 (ISBN)
Description
* Packed with more than forty percent new and updated material,this edition shows business managers, marketing analysts, and datamining specialists how to harness fundamental data mining methodsand techniques to solve common types of business problems
* Each chapter covers a new data mining technique, and then showsreaders how to apply the technique for improved marketing, sales,and customer support
* The authors build on their reputation for concise, clear, andpractical explanations of complex concepts, making this book theperfect introduction to data mining
* More advanced chapters cover such topics as how to prepare datafor analysis and how to create the necessary infrastructure fordata mining
* Covers core data mining techniques, including decision trees,neural networks, collaborative filtering, association rules, linkanalysis, clustering, and survival analysis
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Additional editions
Michael J. Berry | Gordon S. Linoff
Data Mining Techniques
For Marketing, Sales, and Customer Relationship Management
Book
04/2004
2nd Edition
Wiley
€43.90
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Persons
MICHAEL J. A. BERRY and GORDON S. LINOFF are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored some of the leading data mining titles in the field, Data Mining Techniques, Mastering Data Mining, and Mining the Web (all from Wiley). They each have more than a decade of experience applying data mining techniques to business problems in marketing and customer relationship management.
Content
Acknowledgments.
About the Authors.
Introduction.
Chapter 1: Why and What Is Data Mining?
Chapter 2: The Virtuous Cycle of Data Mining.
Chapter 3: Data Mining Methodology and Best Practices.
Chapter 4: Data Mining Applications in Marketing and CustomerRelationship Management.
Chapter 5: The Lure of Statistics: Data Mining Using FamiliarTools.
Chapter 6: Decision Trees.
Chapter 7: Artificial Neural Networks.
Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoningand Collaborative Filtering.
Chapter 9: Market Basket Analysis and Association Rules.
Chapter 10: Link Analysis.
Chapter 11: Automatic Cluster Detection.
Chapter 12: Knowing When to Worry: Hazard Functions and SurvivalAnalysis in Marketing.
Chapter 13: Genetic Algorithms.
Chapter 14: Data Mining throughout the Customer Life Cycle.
Chapter 15: Data Warehousing, OLAP, and Data Mining.
Chapter 16: Building the Data Mining Environment.
Chapter 17: Preparing Data for Mining.
Chapter 18: Putting Data Mining to Work.
Index.
