Companion Pages for Data Mining Techniques (Second Edition)

By Michael J. A. Berry and Gordon S. Linoff
2004 John Wiley & Sons

Data Sets and Course Notes

NYtowns as a tab-delimited text file. 562 variables describing the 1,022 towns in the state of New York. (In New York, a "town" is a subdivision of a county which may or may not correspond to an actual incorporated village or city.)

NYtowns as a SAS dataset.

Definitions from the US census bureau for some of the variables used in the NYtowns data (comma-separated values).

Catalog responders as a tab-delemited text file.

Catalog responders as a SAS dataset.

Definitions for fields in the catalog data.

Subscribers of a wireless phone company for use in survival analysis exercises as a tab-delimited text file.

Subscribers of a wireless phone company for use in survival analysis exercises as a SAS dataset.

Course notes for 3-day data mining class taught by Michael Berry and Gordon Linoff. Includes exercises that make use of the datasets listed above.

Chapter by Chapter Resources

The material in this section is meant to help instructors who are using Data Mining Techniques as a classroom text. Permission is granted to use the illustrations in presentations or classroom notes so long as they are clearly identified as coming from Data Mining Techniques (Second Edition) by Michael J. A. Berry and Gordon S. Linoff, Copyright 2004, John Wiley & Sons.

The PowerPoint presentations offered here are from Professor Ronald J Norman of National University in La Jolla, California. Dr. Norman used these slides in his data mining course based on Data Mining Techniques (Second Edition).

Chapter 1: Why and What is Data Mining

Powerpoint slides from Professor Ronald Norman.

Chapter 2: The Virtuous Cycle of Data Mining

Illustrations for Chapter 2.
Powerpoint slides from Professor Ronald Norman.

Chapter 3:Data Mining Methodology and Best Practices

Illustrations for Chapter 3.
Powerpoint slides from Professor Ronald Norman.

Chapter 4: Business Applications of Data Mining

Illustrations for Chapter 4.
Powerpoint slides from Professor Ronald Norman.

Chapter 5: Data Mining with Familiar Tools

Illustrations for Chapter 5.
Powerpoint slides from Professor Ronald Norman.

Chapter 6:Decision Trees

Illustrations for Chapter 6.
Powerpoint slides from Professor Ronald Norman.
Supplemental material from Dr. Norman's course.

Chapter 7: Neural Networks

Illustrations for Chapter 7.
Powerpoint slides from Professor Ronald Norman.
Supplemental material from Dr. Norman's course.

Chapter 8: Nearest Neighbor Approaches--Memory Based Reasoning and Collaborative Filtering

Illustrations for Chapter 8.
Powerpoint slides from Professor Ronald Norman.

Chapter 9: Association Rules

Illustrations for Chapter 9.
Powerpoint slides from Professor Ronald Norman.

Chapter 10: Link Analysis

Illustrations for Chapter 10.
Powerpoint slides from Professor Ronald Norman.
Supplemental material from Dr. Norman's course.
Supplemental material from Dr. Norman's course.
Supplemental material from Dr. Norman's course.

Chapter 11: Clustering

Illustrations for Chapter 11.
Powerpoint slides from Professor Ronald Norman.

Chapter 12: Survival Analysis

Illustrations for Chapter 12.
Powerpoint slides from Professor Ronald Norman.

Chapter 13: Genetic Algorithms

Illustrations for Chapter 13.
Powerpoint slides from Professor Ronald Norman.

Chapter 14: Finding Customers in Data

Illustrations for Chapter 14.
Powerpoint slides from Professor Ronald Norman.
Supplemental material from Dr. Norman's course.
Supplemental material from Dr. Norman's course.

Chapter 15: Data Mining, Data Warehousing, and OLAP

Illustrations for Chapter 15.
Powerpoint slides from Professor Ronald Norman.
Supplemental material from Dr. Norman's course.