The week's pick
Reseach Article
Indexed Enhancement on GenMax Algorithm for Fast and Less Memory Utilized Pruning of MFI and CFI
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 16 |
| Year of Publication: 2012 |
| Authors: C. Sathya, C. Chandrasekar |
10.5120/5627-7952
|
C. Sathya, C. Chandrasekar . Indexed Enhancement on GenMax Algorithm for Fast and Less Memory Utilized Pruning of MFI and CFI. International Journal of Computer Applications. 41, 16 ( March 2012), 37-41. DOI=10.5120/5627-7952
Abstract
The essential problem in many data mining applications is mining frequent item sets such as the discovery of association rules, patterns, and many other important discovery tasks. Fast and less memory utilization for solving the problems of frequent item sets are highly required in transactional databases. Methods for mining frequent item sets have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent item sets which is too large to fit in memory. GenMax, a search based algorithm is used for mining maximal frequent item sets. GenMax uses a number of optimizations to prune the search space. It uses a technique called progressive focusing to perform maximal checking, and differential set propagation to perform fast frequency computation. The proposal in this paper present an improved index based enhancement on GenMax algorithm for effective fast and less memory utilized pruning of maximal frequent item sets and closed frequent item sets. The proposed model reduce the number of disk I/Os and make frequent item set mining scale to large transactional databases. Experimental results shows a comparison of improved index based GenMax and existing GenMax for efficient pruning of maximal frequent and closed frequent item sets in terms of item precision and fastness.
References
- Go'sta Grahne, and Jianfei Zhu, " Fast Algorithms for Frequent Itemset Mining Using FP-Trees " IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 10, OCTOBER 2005 1347
- J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of ACM SIGMOD'00, pages 1–12, May 2000.
- G. Grahne and J. Zhu. High performance mining of maxi-mal frequent itemsets. In SIAM'03 Workshop on High Performance Data Mining: Pervasive and Data Stream Mining, May 2003.
- R. J. Bayardo, "Efficiently mining long patterns from databases", In ACM SIGMOD Conference, June 1998.
- R. Agrawal, C. Aggarwal, and V. Prasad, "Depth First Generation of Long Patterns", In ACM SIGKDD Conference, August, 2000.
- D. Burdick, M. Calimlim, and J. Gehrke, "MAFIA: a maximal frequent itemset algorithm for transactional databases", In International Conference on Data Engineering, April, 2001.
- Karam Gouda and Mohammed j. Zaki, "GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets" , In IEEE International Conference on Data Mining and Knowledge Discovery, Volume 11, pp. 1–20, 2005.
- J. Pei, J. Han, and R. Mao. CLOSET: An efficient algorithm for mining frequent closed itemsets. In ACM SIGMOD'00 Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 21–30, 2000.
- E. Baralis, T. Cerquitelli, and S. Chiusano, "Index Support for Frequent Itemset Mining in a Relational DBMS", In proceedings of 21st International Conference on Data Engineering (ICDE '05), Tokyo, pp. 754-765, April 2005.
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