Efficient mining of generalized association rules with non-uniform minimum support

Mining generalized association rules between items in the presence of taxonomies has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or for items within the same taxonomy level. This constraint on minimum support would restrain an expert from discovering some deviations or exceptions that are more interesting but much less supported than general trends. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomies to allow any form of user-specified multiple minimum supports. We discuss the problems of using classic Apriori itemset generation and presented two algorithms, MMS_Cumulate and MMS_Stratify, for discovering the generalized frequent itemsets. Empirical evaluation showed that these two algorithms are very effective and have good linear scale-up characteristics.