Decision Trees and Rule-Based Segmentation

taught by Tony Babinec

Brief Description:

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

Instructor(s):
Level: Intermediate

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.

Dates:
January 18, 2013 to February 15, 2013
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Decision Trees and Rule-Based Segmentation

taught by Tony 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.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Business Analytics

• Data Mining

Prerequisite(s):

• Introduction to Predictive Modeling 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 Program:

SESSION 1: Introduction

• Overview of rule induction methods
• APRIORI - Find all associations with at least a specified support, confidence, and number of elements

SESSION 2: Classification via CHAID

• CHAID - Chi-square Automatic Interaction Detection - an exploratory method for large numbers of categorical variables
• Response-based segmentation
• Finding profitable segments
• Understanding the quality of the CHAID solution

SESSION 3: Classification via C4.5 Decision Trees

• C4.5 - A public-domain machine learning method
• Decision trees versus rule sets
• Limitations of tree-based methods
• Successor methods to C4.5

SESSION 4: Rule Construction via Covering Algorithms

• Using covering algorithms to construct rules
• The PRISM rule learner
• INDUCT - A modification of PRISM

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Organization of the Course:

This course takes place over the internet at the Institute for 4 weeks. 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 typically requires 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, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The course text is Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition, by Ian Witten and Eibe Frank. The text can be ordered from the publisher, Morgan Kaufmann/Elsevier, by clicking here.

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.

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Decision Trees and Rule-Based Segmentation

taught by Tony Babinec

Instructor(s):
Dates:
January 18, 2013 to February 15, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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