The Companion Website to 

Data Mining Methods and Models

by
Daniel T. Larose 
Director, Data Mining @CCSU,  
Associate Professor of Statistics, Central Connecticut State University
  larosed@ccsu.edu  

Download Excerpts  from 
Data Mining Methods and Models
Preface Index
Detailed Table of Contents Chapter 
Summaries

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 Michael Christian at mchristian@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.)
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)