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A New Approach on Rare Association Rule Mining

by N. Hoque, B. Nath, D. K. Bhattacharyya
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 3
Year of Publication: 2012
Authors: N. Hoque, B. Nath, D. K. Bhattacharyya
10.5120/8398-2001

N. Hoque, B. Nath, D. K. Bhattacharyya . A New Approach on Rare Association Rule Mining. International Journal of Computer Applications. 53, 3 ( September 2012), 1-6. DOI=10.5120/8398-2001

@article{ 10.5120/8398-2001,
author = { N. Hoque, B. Nath, D. K. Bhattacharyya },
title = { A New Approach on Rare Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 3 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number3/8398-2001/ },
doi = { 10.5120/8398-2001 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:08.852020+05:30
%A N. Hoque
%A B. Nath
%A D. K. Bhattacharyya
%T A New Approach on Rare Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 3
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rule mining is the process of finding some relations among the attributes/attribute values of huge database based on support value. Most existing association mining techniques are developed to generate frequent rules based on frequent itemsets generated on market basket datasets. A common property of these techniques is that they extract frequent itemsets and prune the infrequent itemsets. However, such infrequent or rare itemsets and consequently the rare rules may provide valuable information. So, many applications demand to mine such rare association rules which have low support but higher confidence. This paper presents a method to generate both frequent and rare itemsets and consequently the rules. The effectiveness of the rules has been validated over several real life datasets.

References
  1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. pages 206–216. In ACM SIGMOD International Conference on Management of Data, 1993.
  2. R. Agrawal, T. Imielinski, and A. Vu. Mining association rules with item constraints. pages 67–73. In the Third International Conference on Knowledge Discovery in Databases and Data Mining, 1997.
  3. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. volume 26, pages 255–268. in Proc. of the 1997 ACM SIGMOD Int'n Conf. on Management of data, 1997.
  4. C. A. C Coello. A comprehensive survey of evolutionarybased multi-objective optimization technique. pages 269– 308. Knowledge and information systems, 1999.
  5. C. M. Fonseca and P. L. Fleming and. An overview of of evolutionary algorithms in multi-objective optimization. pages 1–16. Evolutionary Computation 3, 1995.
  6. A. A. Freitas. A survey of evolutionary algorithm for data mining and knowledge discovery. pages 819–845. Advances in Evolutionary Computing, Springer-Verlag,New York, 2003.
  7. J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation:a frequent-pattern tree approach. volume 8, pages 53–87. Data Mining and Knowledge Discovery, 2004.
  8. R. U. Kiran and P. K. Reddy. An improved multiple minimum support based approach to mine rare association rules. pages 340–347. IEEE Symposium on Computational Intelligence and Data Mining, 2009.
  9. R. U. Kiran and P. K. Reddy. Mining rare association rules in the datasets with widely varying items' frequencies. The 15th International Conference on Database Systems for Advanced Applications Tsukuba, Japan, April 1- 4,, 2010.
  10. Y. S. Koh and N. Rountree. Finding sporadic rules using apriori-inverse. pages 97–106. Springer-Verlag Berlin Heidelberg, 2005.
  11. T. S. Kumar, V. Kavita, and T. Ravichandran. Efficient tree based distributed data mining algorithm for mining frequent patterns. volume 10. International Journal of Computer Applications, 2010.
  12. D. I. Lin and Z. M. Kedem. Pincer-search: an efficient algorithm for discovering the maximal frequent set. pages 105–219. In Proc. Of 6th European Conference on Extending DB Tech, 1998.
  13. B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports. pages 337–341. ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations, 1999.
  14. H. Mannila. Methods and problems in data mining. pages 41–55, 1997.
  15. B. Nath, D. K. Bhattacharyya, and A. Gosh. Faster generation of association rules. volume 1, pages 267–279. IJITKM, 2008.
  16. B. Nath and A. Ghosh. Multi-objective rule mining using genetic algorithm. pages 123–133. Information Science 163, 2004.
  17. R. Srikant and R. Agrawala. Mining generalized association rules. pages 407–419. Proceedings of the 21st VLDB Conference Zurich, Swizerland, 1995.
  18. A. Savesere, E. Omiecinski, and S. Navathe. An effective algorithm for mining asociation rules in large database. pages 432–443. In proceedings of International Conference on VLDB95, 1995.
  19. L. Szathmary and P. Valtchev. Towards rare itemset mining. Soutenue publiquement le.
  20. L. Szathmary, P. Valtchev, and A. Napoli. Generating rare association rules using the minimal rare itemsets family. volume 4, pages 219–238. International Journal on Software Informatics, 2010.
  21. H. Yun, D. Ha, B. Hwang, and K. H. Ryu. Mining association rules on significant rare data using relative support. volume 67, pages 181–191. The Journal of Systems and Software, 2003.
  22. E. Zitzler, K. Dev, and L. Thiele. Comparision of multiobjective evolutionary algorithms: empirical results. pages 125–148. Evolutionary Computation 8, 2000.
Index Terms

Computer Science
Information Sciences

Keywords

Association rule rare rule minimum constraint confidence multi-objective

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