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An Analysis on Association Rule Mining Techniques

Published on March 2013 by Ish Nath Jha, Samarjeet Borah
journal_cover_thumbnail

International Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN2012 - Number 4
March 2013
Authors: Ish Nath Jha, Samarjeet Borah
ae6b95ad-171e-44e2-8d6f-0c51185f5def

Ish Nath Jha, Samarjeet Borah . An Analysis on Association Rule Mining Techniques. International Conference on Computing, Communication and Sensor Network. CCSN2012, 4 (March 2013), 40-45.

@article{
author = { Ish Nath Jha, Samarjeet Borah },
title = { An Analysis on Association Rule Mining Techniques },
journal = { International Conference on Computing, Communication and Sensor Network },
issue_date = { March 2013 },
volume = { CCSN2012 },
number = { 4 },
month = { March },
year = { 2013 },
issn = 0975-8887,
pages = { 40-45 },
numpages = 6,
url = { /specialissues/ccsn2012/number4/11278-1043/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Computing, Communication and Sensor Network
%A Ish Nath Jha
%A Samarjeet Borah
%T An Analysis on Association Rule Mining Techniques
%J International Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN2012
%N 4
%P 40-45
%D 2013
%I International Journal of Computer Applications
Abstract

Association rule mining is a subfield of Data mining. It is a popular and widely used method to extract interesting and useful patterns from large sets of data. The first Rule Mining Algorithm was formulated by R. Agrawal in 1993. After the Apriori Algorithm formulated by R. Agrawal, many other algorithms have been proposed. Each of these algorithms has its own advantages and disadvantages over the others. The major issues of concern are the cost efficiency in terms of memory utilization, interestingness of the rules generated, influence of the minimum support level specified on the rules generated, the ability to discover relationships not only quantitatively but also qualitatively and the processing efficiency of the algorithm. This paper provides a comparative analysis on the classical Apriori algorithm along with some other association rule mining algorithms.

References
  1. R. Agrawal, T. Imielinski, and A. N. Swami, “Mining association rules between sets of items in large databases,” in Proc. of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993. ACM Press, 1993, pp. 207–216.
  2. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. of VLDB, 1994, pp. 487–499.
  3. N. Dexters, P. W. Purdom, and D. Van Gucht, “A probability analysis for candidate-based frequent itemset algorithms,” in SAC ’06. New York, NY, USA: ACM, 2006, pp. 541–545.
  4. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in SIGMOD Conference. ACM, 2000, pp. 1–12.
  5. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, “Dynamic itemset counting and implication rules for market basket data,” SIGMOD Rec., Volume. 26, no. 2, pp. 255–264, 1997.
  6. G. Buehrer, S. Parthasarathy, and A. Ghoting, “Out-of-core frequent pattern mining on a commodity pc,” in KDD ’06: Proceedings of the 12th ACM SIGKDD international confer- ence on Knowledge discovery and data mining. New York, NY, USA: ACM, 2006, pp. 86–95.
  7. X. Yan, “Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support”, Expert Systems with Applications, Volume 36, Issue 2, pp: 3066-3076 (2008).
  8. M. Kaya, R. Alhajj, “Genetic algorithm based framework for mining fuzzy association rules”, Fuzzy Sets and Systems 152:3, pp 587-601 (2005).
  9. B. C. Chien, Z. L. Lin and T. P. Hong, “An efficient clustering algorithm for mining fuzzy quantitative association rules”, The Ninth International Fuzzy Systems Association World Congress, pp. 1306-1311 (2001).
  10. C.H. Chen, V.S. Tseng, T.P. Hong, “Cluster-Based Evaluation in Fuzzy-Genetic Data Mining”, IEEE T. Fuzzy Systems 16(1): 249-262 (2008).
  11. B. Goethals, “Ef?cient frequent pattern mining,” Ph.D. Dissertation, Transnationale Universiteit Limburg, 2002.
  12. Sean Chester, Ian Sandler, Alex Thomo: Scalable AprioriBased Frequent Pattern Discovery. CSE (1) 2009: 48-55.
  13. De Cock, M., Cornelis, C., Kerre, E.E.: Fuzzy Association Rules: A Two-Sided Approach. In: FIP, pp 385-390 (2003).
  14. Yan, P., Chen, G., Cornelis, C., De Cock, M., Kerre, E.E.: Mining Positive and Negative Fuzzy Association Rules. In: KES, pp. 270-276. Springer (2004).
  15. De Cock, M., Cornelis, C., Kerre, E.E.: Elicitation of fuzzy association rules from positive and negative examples. Fuzzy Sets and Systems, 149, 73–85 (2005).
  16. Verlinde, H., De Cock, M., Boute, R.: Fuzzy Versus Quantitative Association Rules: A Fair Data-Driven Comparison. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 36, 679-683 (2006).
  17. Dubois, D., Hüllermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Min. Knowl. Discov., 13, 167-192 (2006).
  18. Dubois, D., Hüllermeier, E., Prade, H.: A Note on Quality Measures for Fuzzy Association Rules. In: IFSA, pp. 346-353. Springer-Verlag (2003).
  19. Hüllermeier, E., Yi, Y.: In Defense of Fuzzy Association Analysis. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 37, 1039- 1043 (2007).
  20. Delgado, M., Marin, N., Sanchez, D., Vila, M. A.: Fuzzy Association Rules: General Model and Applications. IEEE Transactions on Fuzzy Systems 11, 214-225 (2003).
  21. Shu-Yue, J., Tsang, E., Yenng, D., Daming, S.: Mining fuzzy association rules with weighted items. In: IEEE International Conference on SMC, pp. 1906-1911, IEEE (2000).
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Support Confidence Apriori AIS FP-Tree