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

Jnanamurthy, H K and Vishesh, H V and Jain, Vishruth and Kumar, Preetham and Pai, Radhika M (2012) Top Down Approach to find Maximal Frequent Item Sets using Subset Creation. The First International Conference on Information Technology Convergence and Services. pp. 445-452.

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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.

Item Type: Article
Uncontrolled Keywords: Data Mining (DM), Frequent Item Set (FIS), Association Rules (AR), Apriori Algorithm (AA), Maximal Frequent Item First (MFIF).
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 22 Mar 2013 08:47
Last Modified: 22 Mar 2013 08:47

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