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

Front Cover
John Wiley & Sons, Apr 12, 2011 - Computers - 888 pages
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—áis 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.á

  • 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.

 

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User Review  - jamet123 - LibraryThing

Anyone interested in automating and improving decisions should have this book. It is one of the classic works on data mining and well worth the read. I really liked the book both because it is well ... Read full review

Contents

Lessons Learned
457
Automatic Cluster Detection
459
Alternative Approaches to Cluster Detection
499
Gaussian Mixture Models
505
Divisive Clustering
513
SelfOrganizing Maps
527
Market Basket Analysis and Association Rules
535
Association Analysis
547

Multiple Comparisons
129
Profiling
151
Transform Data to Bring Information
180
Deploy Models
190
Data Mining Using Classic Statistical Techniques
195
Similarity Models
196
Table Lookup Models
203
Na´ve Bayesian Models
210
Multiple Regression
220
Decision Trees
237
Finding the Best Split
252
Lessons Learned
279
Artificial Neural Networks
281
Lessons Learned
319
MemoryBased
321
Measuring Distance and Similarity
335
Lessons Learned
354
Using Survival Analysis
357
Genetic Algorithms and Swarm Intelligence
397
Pattern Discovery
429
Extending the Ideas
569
Link Analysis
581
Lessons Learned
612
Data Warehousing OLAP Analytic Sandboxes and Data Mining
613
Where Does OLAP Fit In?
639
Building Customer Signatures
655
Making the Data Mean More
693
Combining Variables
707
Lessons Learned
733
Too Much of a Good Thing? Techniques for Reducing the Number of Variables
735
Principal Components
753
Variable Clustering
768
Lessons Learned
774
Text Mining
775
Ad Hoc Text Mining
786
From Text to Numbers
794
Sentiment Analysis
806
Lessons Learned
819
Index
821
Copyright

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About the author (2011)

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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