Discovery of Maximal Frequent Item Sets using Subset Creation

Jnanamurthy, H K and Vishesh, H V and Jain, Vishruth and Kumar, Preetham and Pai, Radhika M (2013) Discovery of Maximal Frequent Item Sets using Subset Creation. International Journal of Data Mining and Knowledge Management Process, 3 (1). pp. 27-38.

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Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like utility, number of items sold and for the mining of association data patterns. In this paper, we propose an efficient algorithm to find maximal frequent itemset first. Most of the association rule algorithms used to find minimal frequent item first, then with the help of minimal frequent itemsets derive the maximal frequent itemsets, these methods consume more time to find maximal frequent itemsets. To overcome this problem, we propose a new approach to find maximal frequent itemset directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent itemsets.

Item Type: Article
Uncontrolled Keywords: Data Mining (DM), Frequent ItemSet (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: 09 Apr 2015 09:46
Last Modified: 09 Apr 2015 09:46

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