Finding of association rules is a crucial problem in data mining. Two sub-problems of mining association rules. First findout frequent itemsets from dataset and then develop association rules based on frequent itemsets. The important factor istime required for finding frequent itemsets. All the previous algorithms are not efficient and scalable for mining frequentitemsets in transaction datasets. In this paper, we provide an unifying feature for generating frequent itemset algorithms. Theperformance analysis with Wine, Hepatitis, Heart datasets. The algorithms analysis using different minimum support,number of rows and columns.
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