Exploring fuzzy ontologies in mining generalized association rules
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
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Association rule mining is sought for items through a fairly large data set relation are certainly consequential. The traditional association mining based on a uniform minimum support, either missed interesting patterns of low support or suffered from the bottleneck of itemset generation. An alternative solution relies on exploiting support constraints which specifies the required minimum support itemsets. This paper proposes an ACS-based algorithm to determine membership functions for each item followed by computing minimum supports. It therefore will run the fuzzy multi-level mining algorithm for extracting knowledge implicit in quantitative transactions, immediately. In order to address this need, the new approach can express three profits includes specifying the membership functions for each items, computing the minimum support for each item regarding to characteristic for each item in database and making a system automation. We considered an algorithm that can cover the multiple level association rules under multiple item supports, significantly.