Rough set model for discovering single-dimensional and multidimensional association rules

Abstract

In this paper, the mining of association rules with rough set technology is investigated as the algorithm RSASM. The RSASM algorithm is introduced for mining of single-dimensional association rules, which is constituted of three steps: (1) generalizing database to discretize quantitative attributes and decrease quantity of data; (2) finding candidate itemsets with the concept of equivalence class derived from indiscernibility relation in rough set theory; and (3) finding frequent itemsets with multiple minimum supports. The RSASM can be expanded to multidimensional association rules mining easily. It can be seen from experiments that the mining algorithm is elegant and efficient, which can obtain more rapid computing speed and sententious rules at the same time.