Exploiting statistically significant dependent rules for associative classification

摘要

Established associative classification algorithms have shown to be very effective in handling categorical data such as text data. The learned model is a set of rules that are easy to understand and can be edited. However, they still suffer from the following limitations: first, they mostly use the support-confidence framework to mine classification association rules which require the setting of some confounding parameters; second, the lack of statistical dependency in the used framework may lead to the omission of many interesting rules and the detection of meaningless rules; third, the rule generation process usually generates a sheer number of rules which puts in question the interpretability and readability of the learned associative classification model.