Research Article
Survey on Frequent Itemset Mining Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 1 - Issue 15 |
| Published: February 2010 |
| Authors: Pramod S., O.P. Vyas |
10.5120/316-484
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Pramod S., O.P. Vyas . Survey on Frequent Itemset Mining Algorithms. International Journal of Computer Applications. 1, 15 (February 2010), 86-91. DOI=10.5120/316-484
@article{ 10.5120/316-484,
author = { Pramod S.,O.P. Vyas },
title = { Survey on Frequent Itemset Mining Algorithms },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 1 },
number = { 15 },
pages = { 86-91 },
doi = { 10.5120/316-484 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A Pramod S.
%A O.P. Vyas
%T Survey on Frequent Itemset Mining Algorithms%T
%J International Journal of Computer Applications
%V 1
%N 15
%P 86-91
%R 10.5120/316-484
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Many researchers invented ideas to generate the frequent itemsets. The time required for generating frequent itemsets plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating frequent itemsets from the algorithm. We have explored the unifying feature among the internal working of various mining algorithms. Some implementations were done with KDD cup Dataset to explore the relative merits of each algorithm. The work yields a detailed analysis of the algorithms to elucidate the performance with standard dataset like Adult, Mushroom etc. The comparative study of algorithms includes aspects like different support values, size of transactions and different datasets.
References
- C. Borgelt. An Implementation of the FP- growth Algorithm. Proc. Workshop Open Software for Data Mining (OSDM’05 at KDD’05, Chicago,IL),1–5.ACMPress, New York, NY, USA 2005.
- C.Borgelt. Keeping Things Simple: Finding Frequent Item Sets by Recursive Elimination. Proc. Workshop Open Software for Data Mining (OSDM’05 at KDD’05, Chicago, IL), 66– 70. ACM Press, New York, NY, USA 2005.
- C.Borgelt. Efficient Implementations of Apriori and Eclat. Proc. 1st IEEE ICDM Workshop on Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL). CEUR Workshop Proceedings 90, Aachen, Germany 2003.
- J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 2003.
- J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In: Proc. Conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, NY, USA 2000.
- R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo. Fast discovery of association rules. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307–328. MIT Press, 1996.
- C.L. Blake and C.J. Merz. UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science, University of California at Irvine, CA, USA1998 http://www.ics.uci.edu/˜mlearn/MLRepository.html- 1998.
- M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. NewAlgorithms for Fast Discovery of Association Rules. Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD’97), 283–296. AAAI Press, Menlo Park, CA, USA 1997.
- R. Agrawal, T. Imielienski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. Proc. Conf. on Management of Data, 207–216. ACM Press, New York, NY, USA 1993.
- C. Borgelt. SaM: Simple Algorithms for Frequent Item Set Mining. IFSA/EUSFLAT 2009 conference- 2009.
- J. Han, and M. Kamber, 2000. Data Mining Concepts and Techniques. Morgan Kanufmann.
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