Proposal of STRIM Improving Rule Induction Method and its Application to Datasets Generated via Partial Correspondence Hypothesis

Abstract

Various data mining models and/or methods have been proposed to date. A statistical test rule induction method (STRIM) has been proposed as one of them, that induces if-then rules hidden in a dataset known as the decision table generated based on a simple hypothesis. This study improves the previous data generation model using a hypothesis similar to human rating and the rule induction method to adapt to real-world datasets. Specifically, 1) the hypothesis is expanded from a complete correspondence hypothesis to a partial correspondence hypothesis. 2) The previous rule induction method is developed into a Bayesian STRIM, that infers and/or explores the causes based on the results. The applied rule induction method's validity and usefulness are confirmed using a verification system. The relationship and difference between Bayesian STRIM against a maximum a posteriori probability estimate and a Bayesian network method are also studied in the rule induction problem.