Abstract
The working of Fuzzy logic is based on reasoning rules which is very inherent to the human way of thinking. The nature of fuzzy logic is that it allows mentioning values without the need of specifying a precise value which certainly is not possible with classical logic. In case of classical logic the membership is set to one class or is represented in the binary form which can be categorized as either member or not. The proposed work focus on the fuzzy modeled sensitive association rule mining by providing optimality with optimal search space algorithm. Fuzzy sets can be used to avoid fixed threshold value by way of allowing “soft” boundaries of intervals. The basic principle of fuzzy sets is that it acts as an interface between numerical value and symbolic value which comprises of linguistic terms. The proposed Sensitive Association rule mining using Fuzzy Partition Algorithm (SAFPA) will obtain more sensitiveness in generating association rules and frequent itemset.
Published Version
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