Applying-Data-Mining-Techniques-Using-Enterprise-Miner_CourseNotes.pdf

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Applying Data Mining
Techniques Using
™
Enterprise Miner
Course Notes
Applying Data Mining Techniques Using Enterprise Miner
™
Course Notes
was developed by Sue Walsh.
Some of the course notes is based on material developed by Will Potts and Doug Wielenga. Additional
contributions were made by John Amrhein, Kate Brown, Iris Krammer, and Bob Lucas. Editing and
production support was provided by the Curriculum Development and Support Department.
SAS and all other SAS Institute Inc. product or service names are registered trademarks
or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other
brand and product names are trademarks of their respective companies.
Applying Data Mining Techniques Using Enterprise Miner
™
Course Notes
Copyright
2002 by SAS Institute Inc., Cary, NC 27513, USA. All rights reserved. Printed
in the United States of America. No part of this publication may be reproduced, stored in
a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or
otherwise, without the prior written permission of the publisher,
SAS Institute Inc.
Book code 58801, course code ADMT, prepared date 05Apr02.
For Your Information
iii
Table of Contents
Course Description ....................................................................................................................... v
Prerequisites ................................................................................................................................ vi
General Conventions ..................................................................................................................vii
Chapter 1
1.1
1.2
Introduction to Data Mining................................................................. 1-1
Background......................................................................................................................1-3
SEMMA.........................................................................................................................1-15
Predictive Modeling Using Decision Trees ........................................ 2-1
Chapter 2
2.1
2.2
2.3
2.4
Introduction to Enterprise Miner .....................................................................................2-3
Modeling Issues and Data Difficulties...........................................................................2-20
Introduction to Decision Trees.......................................................................................2-37
Building and Interpreting Decision Trees ......................................................................2-46
Predictive Modeling Using Regression .............................................. 3-1
Chapter 3
3.1
3.2
Introduction to Regression...............................................................................................3-3
Regression in Enterprise Miner .......................................................................................3-8
Variable Selection................................................................................. 4-1
Chapter 4
4.1
Variable Selection and Enterprise Miner .........................................................................4-3
Predictive Modeling Using Neural Networks ..................................... 5-1
Chapter 5
5.1
5.2
Introduction to Neural Networks .....................................................................................5-3
Visualizing Neural Networks ...........................................................................................5-9
iv
For Your Information
Chapter 6
6.1
6.2
6.3
Model Evaluation and Implementation ............................................... 6-1
Model Evaluation: Comparing Candidate Models...........................................................6-3
Ensemble Models...........................................................................................................6-10
Model Implementation: Generating and Using Score Code ..........................................6-16
Cluster Analysis ................................................................................... 7-1
Chapter 7
7.1
7.2
K-Means
Cluster Analysis................................................................................................7-3
Self-Organizing Maps ....................................................................................................7-24
Association and Sequence Analysis .................................................. 8-1
Chapter 8
8.1
8.2
8.3
Introduction to Association Analysis ...............................................................................8-3
Interpretation of Association and Sequence Analysis ......................................................8-7
Dissociation Analysis (Self-Study) ................................................................................8-24
References ........................................................................................... A-1
Appendix A
A.1 References....................................................................................................................... A-3
Appendix B Index ..................................................................................................... B-1
For Your Information
v
Course Description
This course provides extensive hands-on experience with Enterprise Miner and covers the basic skills
required to assemble analyses using the rich tool set of Enterprise Miner. It also covers concepts
fundamental to understanding and successfully applying data mining methods.
After completing this course, you should be able to
•
identify business problems and determine suitable analytical methods
•
understand the difficulties presented by massive, opportunistic data
•
assemble analysis-flow diagrams
•
prepare data for analysis, including partitioning data and imputing missing values
•
train, assess, and compare regression models, neural networks, and decision trees
•
perform cluster analysis
•
perform association and sequence analysis.
To learn more…
A full curriculum of general and statistical instructor-based training is available at
any of the Institute’s training facilities. Institute instructors can also provide on-site
training.
For information on other courses in the curriculum, contact the SAS Education
Division at 1-919-531-7321, or send e-mail to training@sas.com. You can also find
this information on the Web at www.sas.com/training/ as well as in the SAS
Training Course Catalog.
For a list of other SAS books that relate to the topics covered in this Course Notes,
USA customers can contact our SAS Publishing Department at 1-800-727-3228 or
send e-mail to sasbook@sas.com. Customers outside the USA, please contact your
local SAS office.
Also, see the Publications Catalog on the Web at www.sas.com/pubs for a complete
list of books and a convenient order form.

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