The week's pick
Random Articles
Reseach Article
MSApriori using Total Support Tree Data Structure
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 43 - Number 23 |
| Year of Publication: 2012 |
| Authors: Devashree Rai, Kesari Verma, A. S. Thoke |
10.5120/6433-8868
|
Devashree Rai, Kesari Verma, A. S. Thoke . MSApriori using Total Support Tree Data Structure. International Journal of Computer Applications. 43, 23 ( April 2012), 45-49. DOI=10.5120/6433-8868
Abstract
Association rule mining is one of the important problems of data mining. Single minimum support based approaches of association rule mining suffers from "rare item problem". An improved approach MSApriori uses multiple supports to generate association rules that consider rare item sets. Necessity to first identify the "large" set of items contained in the input dataset to generate association rules results in high storage and processing time requirement. The proposed work overcomes this drawback by storing items and their support values as total support tree data structure, resulting in an algorithm that is more efficient than existing algorithm both in terms of memory requirement as well as in processing time.
References
- M. S. Chen, J. Han, P. S. Yu, "Data mining: an overview from a database perspective", IEEE Transactions on Knowledge and Data Engineering, 1996, 8, pp. 866-883.
- Agrawal, R. , Imielinski, T. , and Swami, A. "Mining association rules between sets of items in large databases. " SIGMOD, 1993, pp. 207-216.
- Agrawal, R. , and Srikanth, R. "Fast algorithms for mining association rules. " VLDB, 1994.
- Weiss, G. M. "Mining With Rarity: A Unifying Framework. " SIGKDD Explorations, 2004, Vol. 6, Issue 1, pp. 7 – 19.
- Liu, B. , Hsu, W. , and Ma, Y. "Mining Association Rules with Multiple Minimum Supports. " SIGKDD Explorations, 1999.
- J. Han, Y. Fu, "Discovery of multiple-level association rules from large database", in the twenty-first international conference on very large data bases, Zurich, Switzerland, 1995, pp. 420-431. W. -K. Chen, Linear Networks and Systems, Belmont, CA Wadsworth, 1993, pp. 123–135.
- Zaki, Mohammed J. ; Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering, vol. 12,no. 3, pp. 372-390, May/June 2000.
- Mannila, H. "Methods and Problems in Data Mining. " ICDT, 1997.
- R. Kiran and P. Reddy "An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules" IEEE 2009.
- I. Kouris, C. Makris, A. Tsakalidis"An improved algorithm for mining association rules using multiple support values" FLAIRS 2003.
- Lee, Hong and Lin "Mining association rules with multiple minimum supports using maximum constraints" Elsevier Science, Nov 2004.
- Coenen and Leng (2004). Data Structures for Association Rule Mining: T-trees and P-trees To appear in IEEE Transaction in Knowledge and Data Engineering.
- R. Rymon, "Search Through Systematic Set Enumeration," Proc. Third Int'l Conf. Principles of Knowledge and Reasoning, pp. 539-550, 1992.
- Uci: Blake, c. l. , & Merz, C. J (1998) UCI repository of machine leaning data bases from www. ics. uci. edu/~mlearn/MLrepository. html.
Index Terms
Keywords