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

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