LGen - A Lattice-Based Candidate Set Generation Algorithm for I/O Efficient Association Rule Mining
نویسندگان
چکیده
Most algorithms for association rule mining are variants of the basic Apriori algorithm One characteristic of these Apriori based algorithms is that candidate itemsets are generated in rounds with the size of the itemsets incremented by one per round The number of database scans required by Apriori based algorithms thus depends on the size of the largest large itemsets In this paper we devise a more general candidate set generation algorithm LGen which generates candidate itemsets of multiple sizes during each database scan We show that given a reasonable set of suggested large itemsets LGen can signi cantly reduce the number of I O passes required In the best cases only two passes are su cient to discover all the large itemsets irrespective of the size of the largest ones
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