The week's pick
Random Articles
Reseach Article
A Search Space Reduction Algorithm for Mining Maximal Frequent Itemset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 82 - Number 9 |
| Year of Publication: 2013 |
| Authors: K. Sumathi, S. Kannan, K. Nagarajan |
10.5120/14146-2288
|
K. Sumathi, S. Kannan, K. Nagarajan . A Search Space Reduction Algorithm for Mining Maximal Frequent Itemset. International Journal of Computer Applications. 82, 9 ( November 2013), 32-36. DOI=10.5120/14146-2288
Abstract
Abstract -Mining of frequent itemset plays important role in data mining applications. The algorithms which are used to generate the frequent patterns must perform efficiently. Because the overall performance of association rule mining based on fast discovery of frequent pattern. Many MFI approaches need to recursively construct many candidates, they also suffer the problem of a large search space, so that the performances for the approaches degrade when the database is massive or the threshold for mining frequent patterns is low. In this paper, an efficient method for discovering the maximal frequent itemsets is proposed which combines a vertical tidset representation of the database with effective pruning mechanisms for search space reduction. It works efficiently when the number of itemsets and tidsets are more. The proposed approach has been compared with GenMax algorithm for mushroom dataset and the results show that the proposed algorithm generates less number of candidate itemsets from which MFIs are obtained. Hence, the proposed algorithm performs effectively and generates maximal frequent patterns faster.
References
- 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
- K. Gouda and M. J. Zaki, "Efficiently Mining Maximal Frequent Itemsets", in Proc. of the IEEE
- D. Lin and Z. M. Kedem, "Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set", In Proceedings of VI Intl. Conference on Extending Database Technology, 1998.
- Don-Lin Yang, Ching-Ting Pan and Yeh-Ching Chung An Efficient Hash-Based Method for Discovering the Maximal Frequent Set
- Agrawal, R. , Aggarwal, C. , and Prasad, V. 2000. Depth first generation of long patterns. In 7th Int'l Conference on Knowledge Discovery and Data Mining, pp. 108–118.
- Roberto Bayardo, "Efficiently mining long patterns from databases", in ACM SIGMOD Conference 1998.
- 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.
- 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.
- Tianming Hu,Sam Yuan Sung b, Hui Xiongc, Qian Fud , Discovery of maximum length frequent itemsets, Journal of Information Sciences 4 February 2007, http://datamining. rutgers. edu/publication/ins2008. pdf
- Jiawei Han, Hong Cheng, Dong Xin , Xifeng Yan, Frequent pattern mining: current status and future Directions, http://www. cs. ucsb. edu/~xyan/papers/dmkd07_frequentpattern. pdf
Index Terms
Keywords