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Comparative Study and Analysis on Frequent Itemset Generation Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 145 - Number 2 |
| Year of Publication: 2016 |
| Authors: Aasma Parveen, Shrikant Tiwari |
10.5120/ijca2016910586
|
Aasma Parveen, Shrikant Tiwari . Comparative Study and Analysis on Frequent Itemset Generation Algorithms. International Journal of Computer Applications. 145, 2 ( Jul 2016), 31-35. DOI=10.5120/ijca2016910586
Abstract
Association mining aspire to extort frequent patterns, interesting correlations, associations or informal structures between the sets of items in the transaction databases or further data repositories. It plays a essential role in spawning frequent item sets from big transaction databases. The finding of interesting association relationship between business transaction records in various business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. It is also utilized to extort hidden knowledge from big datasets. The Association Rule Mining algorithms such as Apriori, FP-Growth needs repeated scans over the whole database. All the input/output overheads that are being generated through repeated scanning the whole database reduce the performance of CPU, memory and I/O overheads. In this paper we have equaled many classical Association Rule Mining algorithms and topical algorithms.
References
- C.-Y. Wang, T.-P. Hong and S.–S. Tseng. “Maintenance of discovered sequential patterns for record deletion”. Intell. Data Anal. pp. 399-410, February 2002.
- R. Agrawal, T. Imielinksi and A. Swami, “Database Mining: a performance perspective”, IEE Transactions on knowledge and Data Engineering, 1993
- Agrawal, R., Imielinski, T., and Swami, A. N. “Mining Association Rules between Sets of Items in Large Databases”. Proceedings of the ACM SIGMOD, International Conference on Management of Data, pp.207- 216, 1993.
- Agrawal. R. and Srikant. R., “Fast Algorithms for Mining Association Rules”, Proceedings of 20th International Conference of Very Large Data Bases. pp.487-499, 1994.
- Jong Park, S., Ming-Syan, Chen, and Yu, P. S. “Using a Hash-Based Method with transaction Trimming for Mining Association Rules”. IEEE Transactions on Knowledge and Data Engineering, 9(5), pp.813-825, 1997.
- M. H. Margahny and A. A. Mitwaly, “Fast Algorithm for Mining Association Rules” in the conference proceedings of AIML, CICC, pp(36-40) Cairo, Egypt, 19-21 December 2005.
- Y. Fu., “Discovery of multiple-level rules from large databases”, 1996.
- F. Bodon, “A Fast Apriori Implementation”, in the Proc.1st IEEE ICDM Workshop on Frequentc Itemset Mining Implementations (FIMI2003, Melbourne,FL).CEUR Workshop Proceedings 90, A acheme, Germany 2003.
- Akhilesh Tiwari, Rajendra K. Gupta, and Dev Prakash Agrawal, “Cluster Based Partition Approach for Mining Frequent Itemsets” in the International Journal of Computer Science and Network Security(IJCSNS), VOL.9 No.6,pp(191-199) June 2009.
- JiaWei Han Micheline Kamber.”Data Mining:Concepts and Techniques”[M].Translated by Ming FAN, XaoFeng MENG etc. mechanical industrial publisher,BeiJing,2001,150-158.
- M. J. Zaki. “Scalable algorithms for association mining”. IEEE Transactions on Knowledge and Data Engineering, 12: 372 –390, 2000.
- Jochen Hipp, Ulrich G¨untzer, and Gholamreza Nakhaeizadeh. “Algorithms for Association Rule Mining – A General Survey and Comparison”.ACM SIGKDD, July 2000, Vol-2, Issue 1, page 58-64.
- Sotiris Kotsiantis, and Dimitris Kanellopoulos. “Association Rules Mining: A Recent Overview”. GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), 2006, pp. 71-82.
- S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, “Dynamic itemset counting and implication rules for market basket data”, SIGMOD Record 26(2), pp. 255–276, 1997.Kim Man Lui, Keith C.C. Chan, and John Teofil Nosek “The Effect of Pairs in Program Design Tasks” IEEE transactions on software engineering, VOL. 34, NO. 2, march/april 2008.
- Eui-Hong Han, George Karypis, and Kumar, V. Scalable “Parallel Data Mining for Association Rules”. IEEE Transaction on Knowledge and Data Engineering, 12(3), pp.728-737, 2000.
- Sanjeev Kumar Sharma and Ugrasen Suman “A Performance Based Transposition Algorithm for Frequent Itemsets Generation” International Journal of Data Engineering (IJDE), Volume (2) : Issue (2) : 2011
- Sujni Paul “An Optimized Distributed Association Rule Mining Algorithm In Parallel and Distributed Data
- Sujni Paul “An Optimized Distributed Association Rule Mining Algorithm In Parallel and Distributed Data Mining With Xml Data For Improved Response Time”.International Journal Of Computer Science And Information Technology, Volume 2, Number 2, April 2010
- Manoj Bahel and Chhaya Dule “Analysis of frequent item set generation process in Apriori & RCS (Reduced Candidate Set) Algorithm” National Conference on Information and Communication Technology, Banglore April 2010
- Sedukhin, S.G., Zekri, A.S. and Myiazaki, T.”Orbital Algorithms and Unified Array Processor for Computing 2D Separable Transforms” Parallel Processing Workshops
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