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Association rule has been an area of active research in the field ofknowledge discovery. Data mining researchers had improved upon the quality ofassociation rule mining for business development by incorporating influentialfactors like value (utility), quantity of items sold (weight) and more for themining of association patterns. In this paper, we propose an efficient approachto find maximal frequent itemset first. Most of the algorithms in literatureused to find minimal frequent item first, then with the help of minimalfrequent itemsets derive the maximal frequent itemsets. These methods consumemore time to find maximal frequent itemsets. To overcome this problem, wepropose a navel approach to find maximal frequent itemset directly using theconcepts of subsets. The proposed method is found to be efficient in findingmaximal frequent itemsets.

Top Down Approach to find Maximal Frequent Item Sets using Subset Creation