The week's pick
Reseach Article
An Innovative Approach for finding Frequent Item sets using Maximal Apriori and Fusion Process and Its Evaluation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 4 |
| Year of Publication: 2012 |
| Authors: Shailendra Chourasia, Rashmi Vishwakarma, Neeraj Shukla, Meghna Utmal |
10.5120/5033-7184
|
Shailendra Chourasia, Rashmi Vishwakarma, Neeraj Shukla, Meghna Utmal . An Innovative Approach for finding Frequent Item sets using Maximal Apriori and Fusion Process and Its Evaluation. International Journal of Computer Applications. 40, 4 ( February 2012), 23-26. DOI=10.5120/5033-7184
Abstract
Frequent pattern mining is a vital branch of Data Mining that supports frequent itemsets, frequent sequence and frequent structure mining. Our approach is regarding frequent itemsets mining. Frequent item sets mining plays an important role in association rules mining. Many algorithms have been developed for finding frequent item sets in very large transaction databases. This paper proposes an efficient SortRecursiveMine (Sorted and Recursive Mine) Algorithm for finding frequent item sets. This proposed method reduces the number of scans in the database by first finding the maximal frequent itemsets in the database and then all its subset consider as frequent according to Apriori property. Then reduce the database by just considering only those transactions which are 1-Itemset frequent but not contain in frequent itemsets and then mine the remaining left frequent itemsets. Our proposed SortRecursiveMine algorithm works well based on recursive condition. Thus it reduces the memory constraints and helps to efficiently mine frequent itemsets in less time. At last we are evaluating this method, and performed an experiment on a real dataset to test the run time of our proposed algorithm.
References
- Ashok Savasere, E. Omiecinski and S. Navathe, “An efficient algorithm for mining association rules in large databases”, Proceedings of the 21st International Conference on Very large database, 1995, pp. 420-431.
- Jia Ling, Koh and Vi-Lang Tu, “ A Tree-based Approach for Efficiently Mining Approximate Frequent Itemsets”, IEEE International Conference on Research Challenges in Information Science, 2010, pp. 25-36.
- Jian Pei ,J. Han, J. Lu, H. Nishio.S.and Tang, “H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases”, ICDM International Conference on Data Mining, ICDM, 2001, pp. 441-448.
- Jiawei Han, Jian Pei, and Yiwen Yin, “Mining Frequent Patterns without Candidate Generation”, Proceedings of ACM SIGMOD Conference, Dallas, TX, 2000, pp.53-87.
- Jong Soo Park, M.S. Chen, and P.S. Yu, “An effective hash based algorithm for mining association rules”, Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, May 22-25, 1995, pp. 175-188.
- Ramesh Agrawal and Ramakrishnan Srikant, “Fast algorithms for mining association rules”, proceedings of the 20th VLDB Conference Santiago,Chille, 1994, pp. 487-499.
- Ramesh Agrawal, Tomasz Imielinski, and A. Swami, “Mining association rules between sets of items in large databases”, ACM-SIGMOD Int. Conf. Management of Data, Washington, D.C., May 1993, pp 207–216.
- Senthil Kumar A.V and R.S.D. Wahidabanu, “A Frequent Item Graph Approach for Discovering Frequent Itemsets“, Proceedings of 2008 IEEE International Conference on Advanced Computer Theory, 2008, pp.952-956.
- Yudho Giri Sucahyo and Gopalan.R, “Efficient Frequent Item Set Mining using a Compressed Prefix Tree with Pattern Growth”, Proceedings of 14th Australian Database Conference, Adelaide, Australia, 2003, pp.95-104
- J.R.Jeba,Dr S.P.Victor, “A Novel Approach for finding Frequent Item Sets with Hybrid Strategies”,International Journal of Computer Applications (0975 – 8887) Volume 17– No.5, March 2011.
- Bharat Gupta,Dr.Deepak Garg,Karun Verma, “A Novel Approach to Mine Frequent Item Sets Using Maximal Apriori and FP-Tree Method”,International Journal of Advance Computing Volume 3,Issue 2,April 2011.
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