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Reseach Article

An Efficient Algorithm for Mining Coherent Association Rules

by Sharada Narra, Siva Ponugoti, Suresh Mullapudi, Madhavi Dabbiru
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 2
Year of Publication: 2014
Authors: Sharada Narra, Siva Ponugoti, Suresh Mullapudi, Madhavi Dabbiru
10.5120/16769-6336

Sharada Narra, Siva Ponugoti, Suresh Mullapudi, Madhavi Dabbiru . An Efficient Algorithm for Mining Coherent Association Rules. International Journal of Computer Applications. 96, 2 ( June 2014), 45-50. DOI=10.5120/16769-6336

@article{ 10.5120/16769-6336,
author = { Sharada Narra, Siva Ponugoti, Suresh Mullapudi, Madhavi Dabbiru },
title = { An Efficient Algorithm for Mining Coherent Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 2 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number2/16769-6336/ },
doi = { 10.5120/16769-6336 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:22:49.805406+05:30
%A Sharada Narra
%A Siva Ponugoti
%A Suresh Mullapudi
%A Madhavi Dabbiru
%T An Efficient Algorithm for Mining Coherent Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 2
%P 45-50
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are many data mining techniques for finding association rules with the predefined minimum support from transaction databases. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules convey common-sense knowledge. Mining association rules without minimum support threshold is an important approximation of the association rule mining problem, which has been recently proposed[1]. In this approach rules are generated based on logical implications and hence fewer and more meaningful rules called coherent rules are reported. An algorithm named ChSearch, is proposed to mine coherent rules for the user specified itemset. In this paper, the same problem is studied with two additional improvements. First, Apriori-based algorithm, namely ECARM for mining all coherent rules is proposed. Second to speed up time consuming candidate generation process an efficient pruning strategy is introduced. The proposed method generates coherent rules in shorter average execution times and fewer database scans, and thereby reduce great amount of I/O time per database scan and hence scaled to large databases. The method has been evaluated using both synthetic and real datasets, and the experimental results demonstrate its effectiveness and efficiency.

References
  1. A. T. H. Sim, M. Indrawan, S. Zutshi, and B. Srinivasan. 2010. Logic-Based Pattern Discovery. IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 6, pp. 798-811
  2. Agrawal, R. , Imielinski, T. , and Swami, A. 1993a. Database mining: A performance perspective. IEEE Trans. Knowledge and Data Eng. Volume: 5, Issue: 6 (Nov. ), 914-925.
  3. Agrawal, R. , Imielinski, T. , and Swami, A. 1993b. Mining association rules between sets if items in massive databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. ACM, Washington D. C. , 207-216.
  4. B. Padmanabhan, A Tuzhilin 1999 Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3):303-318
  5. B. Liu, W. Hsu, and Y. Ma 1999. Mining Association Rules with Multiple Minimum Supports. In Proceedings of ACM SIGKDD, pp. 337-341
  6. G. I. Webb and S. Zhang. 2005. K-Optimal Rule Discovery. Data Mining and Knowledge Discovery, vol. 10, no. 1, pp. 39-79.
  7. Brin, S. , Motwani, R. , and Silverstein, C 1997: Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, Tucson, Arizona, 265-276
  8. Savasere, A. , Omiecinski, E. , Navathe, and S. B. 1998: Mining for strong negative associations in a large database of customer transactions. In: ICDE, pp. 494–502
  9. Wu, X. , Zhang, C. , Zhang, S. 2004 Efficient Mining of Both Positive and Negative Association Rules, ACM Transactions on Information Systems, Vol. 22, No. 3, Pages 381–405.
  10. Aggrawal, C. , Yu, P. 1998. A new framework for itemset generation. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM, Seattle, Washington, 18-24.
  11. Bayardo, B. 1998. Efficiently mining long patterns from databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, Seattle, Washington, 85-93.
  12. Chen, M. , Han, J. , and Yu, P. 1996. Data mining: An overview from a database perspective . IEEE Trans. Knowledge and Data Eng. 8, 6(Nov), 866-881.
  13. Han, Jian. , Pei, J. , and Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, ACM, Dallas, Texas, 1-12.
  14. Srikant, R and Agrawal, R. 1996. Mining quantitative association rules in large relational tables. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, Montreal, Quebec, Canada.
  15. Srikant, R. and Agrawal, R. 1997. Mining generalized association rules. Future Generation Computer Systems 13, 2-3(Nov. ), 161-180.
  16. Sotiris Kotsiantis, Dimitris Kanellopoulos. 2006. Association Rules Mining: A Recent Overview. GESTS International Transactions on Computer Science and Engineering, Vol. 32 (1), pp. 71-82.
  17. Cohen, E. , M. Datar, S. Fujiwara, A. Gionis, R. Indyk, P. Motwani, J. Ullman, and C. Yang: 2000. Finding Interesting Associations without Support Pruning. In Proceedings International Conference on Data Engineering.
  18. H. Mannila. 1998. Database Methods for Data Mining. In Proceedings of Fourth Int'l Conf. Knowledge Discovery and Data Mining.
  19. Y. -H. Hu and Y. -L. Chen. 2006. Mining Association Rules with Multiple Minimum Supports: A New Mining Algorithm and a Support Tuning Mechanism. Decision Support Systems, vol. 42, pp. 1-24.
  20. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. 1997. Dynamic Itemset Counting and Implication Rules for Market Basket Data. In proceedings of SIGMOD Record, vol. 26, pp. 255-264.
  21. C. C. Aggarwal and P. S. Yu, 1998. A New Framework for Itemset Generation. In Proceedings of 17th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS '98), pp. 18-24.
  22. J. Blanchard, F. Guillet, H. Briand, and R. Gras. 2005. Assessing Rule Interestingness with a Probabilistic Measure of Deviation from Equilibrium. In Proceedings of 11th Int'l Symp. Applied Stochastic Models and Data Analysis (ASMDA '05), pp. 191-200.
  23. Brin, S. , Motwani, R. and Silverstein, C. 1999. Beyond Market Baskets: Generalizing Association Rules to Correlations. In Proceedings of the ACM SIGMOD Conference, pp. 265-276.
  24. Chun-Hao Chen, Guo-Cheng Lan, Tzung-Pei Hong, Yui-kai Lin. 2013. Mining high coherent association rules with consideration of support measure Expert Systems with Applications. Elsevier 6531-6537.
Index Terms

Computer Science
Information Sciences

Keywords

Association Rules Minimum Support threshold Coherent Rules Preposition logic