Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd Edition by Michael J. Berry, Gordon S. Linoff

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd Edition by Michael J. Berry, Gordon S. Linoff
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd Edition

by Michael J. Berry, Gordon S. Linoff



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



AUTHOR INFORMATION:

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.



TABLE OF CONTENTS:

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 Customer Relationship Management.

Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools.

Chapter 6: Decision Trees.

Chapter 7: Artificial Neural Networks.

Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoning and 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 Survival Analysis 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.