Pattern discovery based on rule induction and taxonomy generation

One of the most important problems with rule induction methods is that they cannot extract rules, which plausibly represent expert´s decision processes. Here, the characteristics of expert´s rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the concept hierarchy for given classes is calculated. Second, based on the hierarchy, rules for each hierarchical level are induced from data. Then, for each given class, rules for all the hierarchical levels are integrated into one rule.