Data Mining Techniques / Edition 2

Data Mining Techniques / Edition 2

by Michael J. A. Berry, Gordon S. Linoff
     
 

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ISBN-10: 0471470643

ISBN-13: 9780471470649

Pub. Date: 03/29/2004

Publisher: Wiley

  • 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

Overview

  • 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

Product Details

ISBN-13:
9780471470649
Publisher:
Wiley
Publication date:
03/29/2004
Edition description:
REV
Pages:
672
Product dimensions:
7.40(w) x 9.28(h) x 1.38(d)

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.

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