An efficient hash based algorithm for mining closed frequent item sets
نویسندگان
چکیده
Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets, and then forming conditional implication rules among them. Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper for generating frequent item sets we developed improved procedure and result analysis with wine dataset. Our improved procedure is compare with ILLT algorithm and time required for generating item sets is less.
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