Closure mappings and the problem of determining maximal frequent itemsets in data mining
Main Article Content
Abstract
Abstract: In data mining, association rules are considered as a fundamental problem. Process of association rules can be run in two stages. The first stage is to find all the frequent itemsets, and the second stage is to generate association rules. However, with a large database, the number of itemsets will be very large and thus the problem of finding association rules is not feasible. In this paper, the author uses he notation of closure mappings and lattice theory as a mathematical approach to show the applicability of these tools to the data mining. In particular, a method of determining maximal itemsets with the purpose of minimal scanning times of database is presented in the paper.
Keywords: Closure mapping, Intersection lattice, maximal frequent itemset, coatom.References
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[3] Rakesh Agrawal, Ramarkrishnan Srikant, Fast Algorithms for Mining Association Rules, Proceedings of VLDB’94, Santiago, chile, 487-499, 1994
[4] Mohammed J. Zaki, Mitsunori Ogihara, Theoretical Foundations of Associations Rules, Proceeding of 3rd SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Seattle, WA, USA 1998.
[5] Mhammed J Zki, Mining non-redundant Associations Rules, Data Mining and Knowledge Discovery 9 (3), 223-248, 2004.
[6] Mohammed J. Zaki and Ching-Jui Hsiao charm: Efficient Algorithm for Mining Closed Itemsets and Their Lattice Structủe. IEEE Transactions On Knowledge And Data Engineering Vol 17 No 4 April 2005.
[7] Karam Gouda, Mohammed J.Zaki, Genmax: An Efficient Algorithm For Mining Maximal Frequent Itemsets, Data Mining and Knowledge Discovery, 11, 1-20, 2005 ã 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands
[8] S.S.Mantha, Madhuri Rao, Ashwini Anilmane, Anil S. Mane, Mining Maximal Frequent Item Sets, International Journal of Computer Applications (0975-8887), Vol 10-No.3, November 2010.
[9] M.Rajalakshmi,T.Purusothaman, R.Nedunchezhian, Maximal Frequent Itemset Generation Using Segmentation Approach, International Journal of Database Management Systems (IJDMS), Vol.3, No.3, August 2011.