The Companion Website to 

Discovering Knowledge in Data:
An Introduction to Data Mining

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


"This is an excellent introductory book on data mining.  I recommend it for everyone who wants to learn data mining."
-- Journal of Statistical Software

 "Including enough real-world case studies, step-by-step examples of real applications, software examples, and screen shots has definitely added to the learning value of the book."
-- Journal of Biopharmaceutical Statistics

"This flawless chapter on neural networks provides the reader with a good understanding of this class of classification methods."
-- Journal of Biopharmaceutical Statistics

"The book is a good addition to the data mining literature as a very introductory text.  The structure of the text follows the knowledge discovery process, making the book easy to read and comprehend."
-- Briefings in Bioinformatics

"The book by Larose is the first data mining book written by a Ph.D. statistician."
-- Technometrics

"If you need a book to help colleagues understand your data mining procedures and results, this is the one you want to give them."
-- Technometrics

"Does a good job introducing data mining to novices.  It skillfully previews some of the basic statistical issues need to understand data mining techniques."
-- Journal of the American Statistical Association

"An excellent 'white-box' overview of established approaches for data analysis, in which readers are shown how, why, and when the methods work."
-- Choice

"Larose has the making of a good series of books on data mining.  I, for one, look forward to the next two books in the series."
-- Computing Reviews
Download Excerpts from 
Discovering Knowledge in Data!
Preface Chapter One
Detailed Table of Contents Summary Index

Purchase Discovering Knowledge in Data:
 An Introduction to Data Mining
at Amazon.com

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 mchristian@wiley.com or contact your Wiley representative for details.


Table of Contents

1.  An Introduction to Data Mining
2.  Data Preprocessing
3.  Exploratory Data Analysis
4.  Statistical Approaches to Estimation and Prediction
5.  K-Nearest Neighbor
6.  Decision Trees
7.  Neural Networks
8.  Hierarchical and K-Means Clustering
9.  Kohonen Networks
10.  Association Rules
11.  Model Evaluation Techniques
Data Sets Used in the Book:
The adult data set (348 Kb zipped, 3.1 Mb unzipped).
The cars data set (10 Kb).  
The cereals data set (7 Kb).  
The churn data set (319 Kb).


Errata
Chapters 1 - 5

Chapters 6 - 11