The week's pick
Random Articles
Reseach Article
Comparative Study of Apriori Algorithms for Parallel Mining of Frequent Itemsets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 90 - Number 8 |
| Year of Publication: 2014 |
| Authors: Avani M. Sakhapara, Bharathi H. N. |
10.5120/15594-4337
|
Avani M. Sakhapara, Bharathi H. N. . Comparative Study of Apriori Algorithms for Parallel Mining of Frequent Itemsets. International Journal of Computer Applications. 90, 8 ( March 2014), 21-24. DOI=10.5120/15594-4337
Abstract
Apriori Algorithms are used on very large data sets with high dimensionality. Therefore parallel computing can be applied for mining of association rules. The process of association rule mining consists of finding frequent item sets and generating rules from the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and computing resources utilization. Thus majority of parallel apriori algorithms focus on parallelizing the process of frequent item set discovery. The computation of frequent item sets mainly consist of creating the candidates and counting them. The parallel frequent itemsets mining algorithms addresses the issue of distributing the candidates among processors such that their counting and creation is effectively parallelized. This paper presents comparative study of these algorithms.
References
- Khadidja Belbachir, Hafida Belbachir, "The Parallelization of Algorithm Based on Partition Principle for Association Rules Discovery", In Proceedings of International Conference on Multimedia Computing and Systems(ICMCS), IEEE, May 2012.
- Ruowu Zhong, Huiping Wang, "Research of Commonly Used Association Rules Mining Algorithm in Data Mining", In Proceedings of International Conference on Internet Computing and Information Services(ICICIS), IEEE, September 2011.
- V. Umarani, Dr. M. Punithavalli, "A Study On Effective Mining Of Association Rules From Huge Databases", International Journal of Computer Science and Research (IJCR), Vol. 1 Issue 1, 2010.
- Xindong Wu , Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang ,Hiroshi Motoda, "Top 10 Algorithms in Data Mining", Knowledge and Information Systems, Volume 14, Issue 1, pp 1-37, Springer, January 2008.
- Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li, "Parallel Data Mining for Association Rules on Shared-Memory Systems", Data Mining and Knowledge Discovery, Springer, 2001.
- Eui-Hong (Sam) Han, George Karypis, Vipin Kumar, "Scalable Parallel Data Mining for Association Rules", IEEE Transactions on Knowledge and Data Engineering, Volume:12 , Issue: 3, May/June 2000.
- Mohammed J. Zaki, "Parallel and Distributed Association Mining: A Survey", IEEE Concurrency, Vol 7, Issue 4, pp 14-25, October 1999.
- Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li, "Parallel Algorithms for Discovery of Association Rules", Data Mining and Knowledge Discovery, Vol 1, Issue 4, pp 343-373, Springer, December 1997.
- Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li, "A Localized Algorithm for Parallel Association Mining", Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures, ACM 1997.
- Rakesh Agrawal, John C. Shafer, "Parallel Mining of Association Rules", IEEE Transactions on Knowledge and Data Engineering, December 1996.
Index Terms
Keywords