Frequent Itemset Mining on Uncertain Database Using OWA Operator

In today’s modern age, one of the most research focused technique for analyzing frequent pattern from the data is known as Frequent Itemset Mining (FIM). In the past three decades, many algorithms for data analysis have been proposed, and different categories of data came into existence. Now, various FIM algorithms are available to deal with certain, probabilistic or fuzzy data. In this paper, a novel solution named FuzzyApriori using OWA operator (FAOWA) algorithm is proposed to mine frequent items from the fuzzy uncertain transactional database. Earlier numerous techniques have been developed to calculate frequent items on the fuzzy uncertain transactional database by considering fuzzy min/max operator as a basis for minimum support. The fuzzy min/max operators consider only one value, i.e., minimum/maximum value of membership function to calculate support for an itemset, in place of considering all values of membership functions of all items in an itemset. Due to the lack of aggregating multicriteria to form a decision function, in this paper, fuzzy min/max operators are replaced by fuzzy OWA for calculating minimum support. Experiments are performed by using example dataset, and standard available dataset and performance are compared with probabilistic and fuzzy support based algorithms.

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