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Data Sets Used in the
Book:
Adult (zip file, 348 Kb)
Baseball (34 Kb)
Breast Cancer (27 Kb)
California (61 Kb)
Cereals (5 Kb)
Churn (428 Kb)
Clothing Store (7794 Kb)
Crash (50 Kb)
German (149 Kb)
Houses (zip file, 524 Kb)
New York (87 Kb)
Nutrition (151 Kb)
Adopters of the book have access to a special
password-protected website containing the answer keys, PowerPoint presentations of
the chapters, applied data mining projects, quizzes, and other
resources. Contact
Paul Petralia at ppetrali@wiley.com
or contact your Wiley representative for details.
Here follow some graphics from Data Mining Methods and Models.
(Note:
The book is not printed in color.)
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Table of Contents
1. Dimension Reduction Methods
2. Regression Modeling
3. Multiple Regression and Model Building
4. Logistic Regression
5. Naive Bayes Estimation and Bayesian Networks
6. Genetic Algorithms
7. Case Study: Modeling Response to Direct Mail Marketing
Why
Data Mining Methods and Models?
1. White Box Approach: Understanding the underlying
algorithmic and model structures.
2. Hands-On Experience: Learning data mining by doing data
mining.
3. WEKA: Step-by-step guidance on how to use WEKA
open-source software to apply various data mining algorithms.
4. CRISP-DM: Data mining as a process.
5. Chapter Exercises: To check to make sure you really
understand it.
6. Software examples: Clementine, Minitab, SPSS, WEKA.
Errata (soon)
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