Jnanamurthy HK, Vishesh HV, Vishruth Jain, Preetham Kumar and Radhika M. Pai, Manipal University, India

Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maximal frequent item set first. Most of the algorithms in literature used to find minimal frequent item first, then with the help of minimal frequent item sets derive the maximal frequent item sets. These methods consume more time to find maximal frequent item sets. To overcome this problem, we propose a navel approach to find maximal frequent item set directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent item sets.

Data Mining (DM), Frequent Item Set (FIS), Association Rules (AR), Apriori Algorithm(AA), Maximal Frequent Item First (MFIF)