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A New MFI Mining Algorithm with effective Pruning Mechanisms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 6 |
| Year of Publication: 2012 |
| Authors: K. Sumathi, S. Kannan, K. Nagarajan |
10.5120/5549-7617
|
K. Sumathi, S. Kannan, K. Nagarajan . A New MFI Mining Algorithm with effective Pruning Mechanisms. International Journal of Computer Applications. 41, 6 ( March 2012), 42-46. DOI=10.5120/5549-7617
Abstract
Mining of frequent patterns is a basic problem in data mining applications. Frequent Itemset Mining is considered to be an important research oriented task in data mining, due to its large applicability in real world applications. In this paper, a new Maximal Frequent Itemset mining algorithm with effective pruning mechanism is proposed. The proposed algorithm takes vertical tidset representation of the database and removes all the non-maximal frequent item-sets to get exact set of MFI directly. Pruning is done for both search space reduction and minimizing the number of frequency computations. It works efficiently when the number of item-sets and tid-sets are more. The proposed approach has been compared with Mafia algorithm for mushroom dataset and the results shows that the proposed algorithm performs effectively and generates frequent patterns faster. In order to understand the algorithm easily, an example is provided in detail.
References
- Roberto Bayardo, "Efficiently mining long patterns from databases", in ACM SIGMOD Conference 1998.
- R. Agarwal, C. Aggarwal and V. Prasad, "A tree projection algorithm for generation of frequent itemsets", Journal of Parallel and Distributed Computing, 2001.
- K. Gouda and M. J. Zaki, "Efficiently Mining Maximal Frequent Itemsets", in Proc. of the IEEE Int. Conference on Data Mining, San Jose, 2001.
- Gosta Grahne and Jianfei Zhu, "Efficiently using prefix-trees in Mining Frequent Itemsets", in Proc. of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations Melbourne, Florida, USA, November 19, 2003.
- Burdick, D. , M. Calimlim and J. Gehrke, "MAFIA: A maximal frequent itemset algorithm for transactional databases", In International Conference on Data Engineering, pp: 443 – 452, April 2001, doi = 10. 1. 1. 100. 6805
- J. Han, J. Pei, and Y. Yin. "Mining frequent patterns without candidate generation", In ACM SIGMOD Conf. , May 2000.
- R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, "Fast discovery of association rules", Advances in Knowledge Discovery and Data Mining, pages 307-328, MIT Press, 1996.
- V. Ganti, J. E. Gehrke, and R. Ramakrishnan, "DEMON: Mining and Monitoring Evolving Data", ICDE 2000: 439-448
- Aggarwal, C. C. and P. S. Yu, "Mining largeitemsets for association rules", in Bulletin of the IEEE Computer Society Technical Committee onData Engineering, 1998, pp: 23-31. http://citeseerx. ist. psu. edu/viewdoc/summary?doi=10. 1. 1. 48. 306
- Aggarwal C. C and P. S. Yu, "Online generation of association rules", in proceedings of the fourteenth International Conference on Data Engineering, 1998, pp:402-411.
- Park J. S, M. S. Chen, P. S. Yu, "An Effective Hash Based Algorithm for Mining Association Rules",ACM SIGMOD Record, Vol. 24, Issue 2, May 1995, pp: 175-186, ISSN: 0163-5808.
- Dunkel B. and N. Soparkar, "Data Organization and access for efficient data mining", in the proceedings of the 15th International Conference on Data Engineering, pp: 522-529, 1999, ISBN: 0-7695-0071-4
- R. Agrawal, T. Imielienski and A. Swami, "Mining association rules between sets of items in largedatabases. In P. Bunemann and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, Pages 207-216, Newyork, 1993, ACM Press.
- Dao-I Lin & Zvi M. Kedem. 1998. Pincer-search: A new algorithm for discovering the maximum frequent set. In 6th Intl. Conf. on Extending Database Technology, pp. 105–119.
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