Association Rules Mining Based on Clustering Analysis and Soft Sets

Bo Li, Zheng Pei, Keyun Qin

Abstract: One challenging problem in data mining is effective association rules mining with predefined minimum support and confidence thresholds from huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use CFSFDP clustering method to classify transaction database, then we use soft sets to describe and a parameterized treatment of the classified transaction database, by considering logical formulas over the soft sets, we can extract useful association rules from the classified transaction database. We use Adult Data Set to illustrate the newly proposed method is an alternative association rules mining method.