The leading introductory book on data mining, fully updated and
revised!
When Berry and Linoff wrote the first edition of Data Mining
Techniques in the late 1990s, data mining was just starting to
move out of the lab and into the office and has since grown to
become an indispensable tool of modern business. This new
edition–more than 50% new and revised– is a
significant update from the previous one, and shows you how to
harness the newest data mining methods and techniques to solve
common business problems. The duo of unparalleled authors share
invaluable advice for improving response rates to direct marketing
campaigns, identifying new customer segments, and estimating credit
risk. In addition, they cover more advanced topics such as
preparing data for analysis and creating the necessary
infrastructure for data mining at your company.
* Features significant updates since the previous edition and
updates you on best practices for using data mining methods and
techniques for solving common business problems
* Covers a new data mining technique in every chapter along with
clear, concise explanations on how to apply each technique
immediately
* Touches on core data mining techniques, including decision
trees, neural networks, collaborative filtering, association rules,
link analysis, survival analysis, and more
* Provides best practices for performing data mining using simple
tools such as Excel
Data Mining Techniques, Third Edition covers a new data
mining technique with each successive chapter and then demonstrates
how you can apply that technique for improved marketing, sales, and
customer support to get immediate results.
Table des matières
Introduction xxxvii
Chapter 1 What Is Data Mining and Why Do It? 1
Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management 27
Chapter 3 The Data Mining Process 67
Chapter 4 Statistics 101: What You Should Know About Data 101
Chapter 5 Descriptions and Prediction: Profi ling and Predictive Modeling 151
Chapter 6 Data Mining Using Classic Statistical Techniques 195
Chapter 7 Decision Trees 237
Chapter 8 Artifi cial Neural Networks 281
Chapter 9 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering 321
Chapter 10 Knowing When to Worry: Using Survival Analysis to Understand Customers 357
Chapter 11 Genetic Algorithms and Swarm Intelligence 397
Chapter 12 Tell Me Something New: Pattern Discovery and Data Mining 429
Chapter 13 Finding Islands of Similarity: Automatic Cluster Detection 459
Chapter 14 Alternative Approaches to Cluster Detection 499
Chapter 15 Market Basket Analysis and Association Rules 535
Chapter 16 Link Analysis 581
Chapter 17 Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining 613
Chapter 18 Building Customer Signatures 655
Chapter 19 Derived Variables: Making the Data Mean More 693
Chapter 20 Too Much of a Good Thing? Techniques for Reducing the Number of Variables 735
Chapter 21 Listen Carefully to What Your Customers Say: Text Mining 775
Index 821
A propos de l’auteur
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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