Decision Trees and Rule-Based Segmentation

Dr. Anthony Babinec

Aim of Course:

Rule induction is an important component of data mining, and this course covers two main styles of generating rules.

One style of machine learning is association learning. In association learning, the learning method searches for any association between features. That is, there is no specific target variable. An example is the recommendation systems used in many online shopping systems - If you bought X, then you may also like Y. We will look at the industry standard method: APRIORI.

A second style of machine learning is classification learning. In classification learning, a learning scheme takes a set of classified examples from which it is expected to learn a way of classifying unseen examples. These are forms of supervised learning, in which there is a specific target variable. We will look at two decision tree methods: C4.5 and CHAID. We will also look at some machine learning methods, such as PRISM and INDUCT.

Rule induction methods have a number of strong pluses going for them: They produce interpretable results; they are flexible and make no strong assumptions about model form; they perform well in practice.

Who Should Take This Course:

Analysts and researchers who need to know more about automated machine learning methods for generating association and decision rules: data miners, consultants, ecommerce analysts, market researchers, direct marketers, diagnosticians, more.

For those enrolled in Professional Advancement Programs, this is a required or elective course in the following Programs:

• Data Mining - elective
• Statistics in Business & Marketing - elective

Course Program:

The course is structured as follows

The Instructor:

Dr. Anthony Babinec, President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Tony has multiple degrees from the University of Chicago, where he studied Advanced Statistics and Survey Research. Before forming AB Analytics, Babinec was Director of Director of Business Development and Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS. He has presented at the AMA's Applied Research Methods Conference, the AMA's ART Forum, Henry Stewart Conferences, the Sawtooth Software Conference, Statistical Innovation's Statistical Modeling Week, and numerous professional meetings. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President. He is on the Editorial Board of the Journal of Targeting, Measurement and Analysis for Marketing.

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 10-15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Professional Advancement Program that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Jan. 23 - Feb. 20, 2009
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 10-15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Intermediate

Prerequisite:

The equivalent of Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners).

Related courses: Introduction to Data Mining gives a general framework for supervised learning methods (classification and prediction, including some coverage of trees). Data Mining: Unsupervised Techniques gives a general framework for unsupervised methods (including some coverage of association rules). Neither course will go into the depth that this course does.

Course Text:

The course text is Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, by Ian Witten and Eibe Frank. The text can be ordered from the publisher, Morgan Kaufmann/Elsevier, by clicking here. Morgan Kaufmann/Elsevier typically offers a 20% discount to the textbook when ordered using the above link.

Software:

Course illustrations and homework assignments may be completed using "Rule Discovery System" (RDS) or the open source package WEKA. For information on obtaining RDS, WEKA and other software, click here.

Registration:

Register Online - $449
Register Online (academic) - $349 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.