MOGACAR: A Method for Filtering Interesting Classification Association Rules

Diana Benavides-Prado

Abstract: Knowledge Discovery process is intended to provide valid, novel, potentially useful and finally understandable patterns from data. An interesting research area concerns the identification and use of interestingness measures, in order to rank or filter results and provide what might be called better knowledge. For association rules mining, some research has been focused on how to filter itemsets and rules, in order to guide knowledge acquisition from the user’s point of view, as well as to improve efficiency of the process. In this paper, we explain MOGACAR, an approach for ranking and filtering association rules when there are multiple technical and business interestingness measures; MOGACAR uses a multi-objective optimization method based on genetic algorithm for classification association rules, with the intention to find the most interesting, and still valid, itemsets and rules.