An Improved Algorithm for Mining Association Rules Using Multiple Support Values

摘要

Almost all the approaches in association rule mining suggested the use of a single minimum support, technique that either rules out all infrequent itemsets or suffers from the bottleneck of generating and examining too many candidate large itemsets. In this paper we consider the combination of two well-known algorithms, namely algorithm DIC and MSApriori in order to end up with a more effective and fast solution for mining association rules among items, with different support values. Experiments show that the new algorithm is better than algorithm MSApriori, as well as better than algorithm DIC.