Companion Pages for Data Mining Techniques for Marketing, Sales, and Customer Relationship Management
(Second Edition)
By
Michael J. A. Berry
and
Gordon S. Linoff
2004 John Wiley & Sons |
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Data Sets and Course Notes
NYtowns
as a csv file with 152 variables
describing 1,006 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
for fields in the NYtowns data (comma-separated
values).
Catalog
responders as a csv 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 csv
file.
Subscribers
of a wireless phone company for use in survival
analysis exercises as a SAS dataset.
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). If others have material they would like to share on this site, please send e-mail to [email protected].
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.
Chapter 16: The Data Mining Environment
Illustrations
for Chapter 16.
Powerpoint slides
from Professor Ronald Norman.
Chapter 17: Data Preparation
Illustrations
for Chapter 17.
Powerpoint slides
from Professor Ronald Norman.
Chapter 18: Putting Data Mining to Work
Illustrations
for Chapter 18.
Powerpoint slides
from Professor Ronald Norman.
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