Mining generalized association rules using pruning techniques

Abstract

The goal of the paper is to mine generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we try 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 beforehand In the proposed algorithm GMAR, we use join methods and pruning techniques to generate new generalized association rules. Through several comprehensive experiments, we find that the GMAR algorithm is much better than BASIC and Cumulate algorithms.