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AbstractÐAssociation rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets and then, forming conditional implication rules among them. In this paper, we present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items aredoi:10.1109/69.846291 fatcat:cnmc63m4zvfdjbcfde7lmulory