Comparative Analysis of Single and Multiple Minimum Support Based Association Rule Mining Algorithms
Devashree Rai1, Kesari Verma2, A. S. Thoke3
1Devashree Rai, Department of Electrical Engineering, National Institute of Technology Raipur, Raipur, India.
2Kesari Verma, Department of Computer Application, National Institute of Technology Raipur, Raipur, India.
3A.S. Thoke, Department of Electrical Engineering, National Institute of Technology Raipur, Raipur, India.
Manuscript received on March 02, 2012. | Revised Manuscript received on March 31, 2012. | Manuscript published on April 30, 2012. | PP:247-250 | Volume-1 Issue-4, April 2012 | Retrieval Number: D0344041412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Association rule mining techniques discover associations between entities. Some techniques are single minimum support based while some are multiple minimum support based. Single minimum support based approach suffer from rare item problem dilemma while multiple support based approach considers rare items for mining association rules. In this paper we have evaluated performance of Apriori-T and MSApriori-T Algorithms that are single and multiple minimum based approaches respectively. These Algorithms uses an efficient data storage mechanism Total support tree for storing item sets.
Keywords: Apriori-T, Association rule mining, MSApriori-T, Total support tree.