Algorithm for mining association rules with multiple minimum supports

Abstract

Aiming at the problem that traditional methods with only one minsup can not completely reflect different appearing frequencies and natures of different data items, based on FP-Tree, a new algorithm is proposed called MSDMFIA (Multiple minimum Supports for Discover Maximum Frequent Item sets Algorithm), The algorithm allows users to specify multiple minsups to reflect various items natures. Through mining the maximum frequent item sets, calculating minsups of the maximum candidate frequent item sets, the association rules can be discovered. The algorithm resolves the bottlenecks in traditional algorithms, e.g., the rare item problem, the frequent generation of candidate item sets and database scanning. Experimental results show that functionality and performance of the proposed algorithm is significantly improved compared with existing algorithms.