Course on Decision Trees and Rule-Based Segmentation


  • 18 January 2013
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Please note this is a four week course ending on 15th February 2013.

Course tutor: Tony Babinec

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):

•Data Analytics

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