The week's pick
Random Articles
Reseach Article
Article:An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 9 - Number 9 |
| Year of Publication: 2010 |
| Authors: Sunil Joshi, Dr. R. S . Jadon, Dr. R. C. Jain |
10.5120/1410-1904
|
Sunil Joshi, Dr. R. S . Jadon, Dr. R. C. Jain . Article:An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function. International Journal of Computer Applications. 9, 9 ( November 2010), 37-41. DOI=10.5120/1410-1904
Abstract
An Important Problem in Data Mining in Various Fields like Medicine, Telecommunications and World Wide Web is Discovering Patterns. Frequent patterns mining is the focused research topic in association rule analysis. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori Algorithm. Most of the previous studies adopt Apriori-like algorithms which generate-and-test candidates and improving algorithm strategy and structure but no one concentrate on the structure of database. A simple approach is if we implement in Transposed database then result is very fast. Recently, different works proposed a new way to mine patterns in transposed databases where a database with thousands of attributes but only tens of objects. In this case, mining the transposed database runs through a smaller search space. In this paper, we systematically explore the search space of frequent patterns mining and represent database in transposed form. We developed an algorithm (termed DFPMT—A Dynamic Approach for Frequent Patterns Mining Using Transposition of Database) for mining frequent patterns which are based on Apriori algorithm and used Dynamic function for Longest Common Subsequence [1]. The main distinguishing factors among the proposed schemes is the database stores in transposed form and in each iteration database is filter /reduce by generating LCS of transaction id for each pattern. Our solutions provide faster result. A quantitative exploration of these tradeoffs is conducted through an extensive experimental study on synthetic and real-life data sets.
References
- Sunil Joshi et al: accepted research paper in The IEEE 2010 International Conference on Communication software and Netweorks (ICCSN 2010) on “A Dynamic Approach for Frequent Pattern Mining Using Transposition of Database” from 26 - 28 February 2010
- B. Jeudy and F. Rioult, Database transposition for constrained closed pattern mining, in: Proceedings of Third International Workshop on Knowledge Discovery in Inductive Databases (KDID) co-located with ECML/PKDD, 2004.
- R. Agrawal, R. Srikant, Fast algorithms for mining association rules,In Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487–499.
- J. Han, Research challenges for data mining in science and engineering. In NGDM 2007.
- R. Agrawal, R. Srikant, Mining sequential patterns, In Proceedings of the 11th International Conference on Data Engineering, 1995, pp. 3
- A fast APRIORI implementation Ferenc Bodon∗ Informatics Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences H-1111 Budapest, L´agym´anyosi u. 11, Hungary
- B. Goethals. Survey on frequent pattern mining. Technical report, Helsinki Institute for Information Technology,03.
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. The International Conference on Very Large Databases, pages 487–499, 1994.
- Improving Frequent Patterns Mining by LFP XU Yusheng, MA Zhixin, CHEN Xiaoyun, LI Lian School of Information Science and Engineering Lanzhou University Lanzhou, China, 730000 e-mail:{xuyusheng, mazhx, chenxy, lil}@lzu.edu.cn Tharam S. Dillon School of Information System Curtin University Perth, Australia
- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Conference on Management of Data, pages 207–216, 1993.
- R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307–328, 1996.
- R. Agrawal and R. Srikant. Mining sequential patterns. In P. S. Yu and A. L. P. Chen, editors, Proc. 11th Int. Conf. Data Engineering, ICDE, pages 3–14. IEEE Press, 6–10 1995.
- F. Bodon and L. R´onyai. Trie: an alternative data structure for data mining algorithms. to appear in Computers and Mathematics with Applications, 2003.
- S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. SIGMOD Record (ACM Special Interest Group on Management of Data),26(2):255, 1997.
- D. W.-L. Cheung, J. Han, V. Ng, and C. Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating technique. In ICDE, pages 106–114, 1996.
- J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In W. Chen, J. Naughton, and P. A. Bernstein, editors, 2000 ACM SIGMOD Intl. Conference onManagement of Data, pages 1–12. ACM Press, 05 2000.
- R. Agarwal, C. Aggarwal, and V. V. V. Prasad: A Tree Projection Algorithm for Generation of Frequent Itemsets.Journal of Parallel and Distributed Computing (special issue on high performance data mining), (to appear), 2000.
- R. Agrawal, T. Imielinski, and R. Srikant: Mining association rules between sets of items in large databases. SIGMOD, May 1993.
- D. Burdick, M. Calimlim, J. Gehrke. MAFIA: A maximal frequent itemset algorithm for transactional databases. In Proc. of 17th Int’l Conf. on Data Engineering, pp. 443-452, 2001.
- Efficient Mining of Weighted Frequent Pattern Ove Data Streams Farhan Ahmed,Tanbeer 2009
Index Terms
Keywords