Preknowledge-based generalized association rules mining

Abstract

The subject of this paper is the mining of generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we attempt to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated in advance. The primary challenge of designing an efficient mining algorithm is how to make use of the original frequent itemsets and association rules to directly generate new generalized association rules, rather than re-scanning the database. In the proposed algorithms GMAR (Generalized Mining Association Rules) and GMFI (Generalized Mining Frequent Itemsets), we use join methods and/or pruning techniques to generate new generalized association rules. After several comprehensive experiments, we find that both algorithms are much better than BASIC and Cumulate algorithms, since they generate fewer candidate itemsets, and furthermore the GMAR algorithm prunes a large amount of irrelevant rules based on the minimum confidence.