Efficient method for mining multiple-level and generalized association rules

Abstract

This paper proposes a idea for mining multiple-level and generalized association rules. First, an item correlation model is set up, based on the domain knowledge and clusters the items according to their correlation. Secondly, the transaction database, based on the item clusters, are reduced which make the transaction database smaller. Finally, the partitioned transaction databases are projected onto a compact structure called AFOPT-tree and find the frequent itemsets from the AFOPT. Based on the proposed idea, this paper proposes a top-down algorithm TD-CBP-MLARM and a bottom-up algorithm BU-CBP-MLARM to mine the multiple-level association rules. Additionally, this paper extends the idea to a generalized mining association rule and gives a new efficient algorithm CBP-GARM. The experiments show that the proposed algorithms not only corrects and completes mining results, but also outperform the well-known and current algorithms in mining effectiveness. © 2011 ISCAS.