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Genetic-fuzzy mining (GFM) was proposed to train fuzzy membership functions, thus enhancing the final solution quality. However, it is quite time-consuming because of frequent database scans for fitness evaluation. In this paper, we propose a parallel algorithm with MapReduce architecture to further speed up the genetic-fuzzy mining process. In the proposed approach, the master processor randomly...
Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested...
Since items may have their own characteristics, different minimum support values and membership functions may be specified for different items. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is designed for finding minimum support values, membership functions, and fuzzy association rules. Possible solutions are evaluated by...
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