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A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements

Authors: Thien Le, Frederic Stahl, Chris Wrench and Mohamed Gaber

Abstract:

Induction of descriptive models is one of the most important technologies in data mining. The expressiveness of descriptive models are of paramount importance in applications that examine the causality of relationships between variables. Most of the work on descriptive models has concentrated on less expressive approaches such as clustering algorithms or rule-based approaches that are limited to a particular type of data, such as association rule mining for binary data. However, in many applications its important to understand the structure of the produced model for further human evaluation. In this research we present a novel generalised rule induction method that allows the induction of descriptive and expressive rules directly from both categorical and numerical features.

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