Optimized Frequent Pattern Mining for Classified Data Sets
Mining frequent patterns in data is a useful requirement in several applications to guide future decisions. Association rule mining discovers interesting relationships among a large set of data items. Several association rule mining techniques exist, with the Apriori algorithm being common. Numerous algorithms have been proposed for efficient and fast association rule mining in data bases, but these seem to only look at the data as a set of transactions, each transaction being a collection of items. The performance of the association rule technique mainly depends on the generation of candidate sets.