Eliminating redundancy in generation of association rules for on-line mining

摘要

A computer method of removing simple and strict redundant association rules generated from large collections of data. A compact set of rules is presented to an end user which is devoid of many redundancies in the discovery of data patterns. The method is directed primarily to on-line applications such as the Internet and Intranet. Given a number of large itemsets as input, simple redundancies are removed by generating all maximal ancestors, the frontier set, for each large itemset. The set of maximal ancestors share a hierarchical relationship with the large itemset from which they were derived and further satisfy an inequality whereby the ratio of respective support values is less than the reciprocal of some user defined confidence value. The resulting compact rule set is displayed to an end user at some specified level of support and confidence. The method is also able to generate the full set of rules from the compact set.