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Reseach Article

An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data

by N. Kavitha, S. Karthikeyan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 14
Year of Publication: 2012
Authors: N. Kavitha, S. Karthikeyan
10.5120/7695-1023

N. Kavitha, S. Karthikeyan . An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data. International Journal of Computer Applications. 49, 14 ( July 2012), 22-25. DOI=10.5120/7695-1023

@article{ 10.5120/7695-1023,
author = { N. Kavitha, S. Karthikeyan },
title = { An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 14 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number14/7695-1023/ },
doi = { 10.5120/7695-1023 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:15.553843+05:30
%A N. Kavitha
%A S. Karthikeyan
%T An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 14
%P 22-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Database is a repository of information. Retrieving automatic patterns from the database provide the requisite information and are in great demand in various domains of science and engineering. The effective pattern mining methods such as pattern discovery and association rule mining have been developed and find its applicability in a wide gamut ranging from science to medical to military and to engineering applications. Contemporary methods of retrieval such as pattern discovery and association rule mining algorithms are useful only for retrieving the data. The limitations of using these techniques are that they are unable to provide a complete association and relationship among the diverse patterns that is retrieved. This paper attempts a solution to the above limitation by designing a new algorithm (CFIM) which generates closed frequent patterns and its associated data concurrently. CFIM makes explicit the relationship between the patterns and its associated data.

References
  1. Rakesh Agrawal, Tomaz Lmielinski and Arun Swami," Mining association rules between sets of items in large databases", Proc of ACM SIGMOD Conference on Management of Data, Washington, 1993.
  2. R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. 1995 Int'l Conf. Data Eng. (ICDE '95), pp. 3-14, Mar. 1995.
  3. J. S. Park, M. Chen, P. S. Yu," An effective hash based algorithm for mining association rules", Proc. of ACM SIGMOD International Conference on Management of Data, May 1995.
  4. Jiawei Han, Ian Pei and Yiwen Yin,"Mining Patterns without Candidate Generation", Proc. of 2000, International Conference on Management of Data, May 16-18, 2000 Dallas, Texas, USA.
  5. S. Brin, R. Motwani, and R. Silverstein, "Beyond Market Basket: Generalizing Association Rules to Correlations," Proc. ACM-1997.
  6. "MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases", Proc. 2001 International Conference on Data Engineering (ICDE'01), pp: 443-452.
  7. R. J. Bayardo," Efficiently Mining Long Patterns from Databases", Proc. of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, Washington, United states.
  8. Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lotfi Lakhal," Efficient mining of association rules using closed itemset lattices", Information System, Vol 24, 1999.
  9. M. J. Zaki and C. -J. Hsiao, "Charm: An Efficient Algorithm for Closed Itemsets Mining," Proc. Second SIAM Int'l Conf. Data Mining, Apr. 2002.
  10. Jiawei Han and Micheline Kamber, "DATA MINING Concepts and Techniques" Elsevier Publishers, 2001 edition.
  11. A. K. C. Wong, Fellow, IEEE, and Gary C. L. Li "Simultaneous pattern and data clustering for pattern cluster analysis" IEEE Trans. Knowledge and Data Eng. , vol. 20, no. 7, pp. 911-923, JULY 2008.
  12. M. J. Zaki, "Mining Non-Redundant Association Rules," Data Mining and Knowledge Discovery, vol. 9, no. 3, pp. 223-248, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Frequent Closed Itemsets and Pattern Discovery