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Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach

Basheer Mohamad Al-Maqaleh Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais12-450873
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  1. Basheer Mohamad Al-Maqaleh. Article: Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach. International Journal of Applied Information Systems 5(3):47-52, February 2013. BibTeX

    @article{key:article,
    	author = "Basheer Mohamad Al-Maqaleh",
    	title = "Article: Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 3,
    	pages = "47-52",
    	month = "February",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Association rule mining is considered as one of the important tasks of data mining intended towards decision making process. It has been mainly developed to identify interesting associations and/or correlation relationships between frequent itemsets in datasets. A multi-objective genetic algorithm approach is proposed in this paper for the discovery of interesting association rules with multiple criteria i. e. support, confidence and simplicity (comprehensibility). With Genetic Algorithm (GA), a global search can be achieved and system automation is developed, because the proposed algorithm could identify interesting association rules from a dataset without having the user-specified thresholds of minimum support and minimum confidence. The experimental results on various types of datasets show the usefulness and effectiveness of the proposed algorithm.

Reference

  1. Bramer, M. 2007. Principles of Data Mining. Springer-Verlag London Limited.
  2. Tan, P–N, Steinbach, M. and Kumar V. 2006. Introduction to Data Mining. Addison–Wesley.
  3. Fayyad, U. and Uthurusamy, R. 1996 . Data mining and knowledge discovery in databases. Communications of the ACM, vol. 39, no. 11, pp. 24–34.
  4. Han, J. and Kamber, M. 2006. Data Mining Concepts and Techniques. Morgan Kaufmann.
  5. Agrawal, R. , Imielinski, T. and Swami, T. 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD' 93), pp. 207–216.
  6. Koh, Y. S. and Rountree, N. 2009. Rare association rule mining: An overview. In Yun, S. K. and Nathan R. (Eds. ), Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, ISBN: 978-1-60566-754-6, Information Science Reference. pp. 1-14.
  7. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
  8. Sivanandam, S. N. and Deepa, S. N. 2008. Introduction to Genetic Algorithms. Springer-Verlag Berlin Heidelberg.
  9. Bharadwaj, K. K. , Hewahi, N. M. and Brando, M. A. 1996. Adaptive hierarchical censored production rule-based system: A genetic algorithm approach. Advances in Artificial Intelligence, SBIA '96, Lecture Notes in Artificial Intelligence, no. 1159, Berlin, Germany, Springer-Verlag, pp. 81-90.
  10. Frietas, A. A. 2002. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg.
  11. Al-Maqaleh, B. M. 2012. Genetic algorithm approach to automated discovery of comprehensible production rules. In Proceedings of the 2nd International Conference on Advanced Computing & Communication Technologies (ACCT2012), IEEE Computer Society, Rohtak, India, pp. 69-71.
  12. Carvalho, D. R. and Frietas, A. A. 2002. A genetic algorithm for discovering small-disjunct rules in data mining. Applied Soft Computing, vol. 2, no. 1, pp. 75-88.
  13. Sarkar, B. K. , Sana, S. S. and Chaudhuri, K. 2012. A genetic algorithm-based rule extraction system. Applied Soft Computing, vol. 12, pp. 238-254.
  14. Saroj, and Bharadwaj, K. K. 2009. Discovery of exceptions: A step towards perfection. In Proceedings of the 3rd International Conference on Network and System Security, IEEE Computer Society, pp. 540-545.
  15. Dehuri, S. and Mall, R. 2006. Predictive and comprehensible rule discovery using a multi objective genetic algorithm. Knowledge Based Systems, vol. 19, pp. 413-421.
  16. Al-Maqaleh, B. M. and Bharadwaj, K. K. 2007. Evolutionary approach to automated discovery of censored production rules with fuzzy hierarchy. In Proceedings of the International Conference on Data Mining and Applications (ICDMA'2007), Hong Kong, China, vol. 1, pp. 716-721.
  17. Au, W. and Chan, K. 2002. An evolutionary approach for discovering changing patterns in historical data. In Proceedings of 2002 SPIE, vol. 4730, pp. 398–409.
  18. Noda, E. , Freitas, A. A. and Lopes, H. S. 1999. Discovering interesting prediction rules with a genetic algorithm. In Proceedings of 1999 Congress on Evolutionary Computation (CEC' 99), pp. 1322-1329.
  19. Al-Maqaleh, B. , Al-Dhobai, M. and Shabazakia, H. 2012. An evolutionary algorithm for automated discovery of small-disjunct rules. International Journal of Computer Applications, vol. 41, issue 8, pp. 33-37.
  20. Al-Maqaleh, B. and Shabazakia, H. 2012. A genetic algorithm for discovering classification rules in data mining. International Journal of Computer Applications, vol. 41, issue 18, pp. 40- 44.
  21. Yan, X. , Zhang, C. and Zhang, S. 2009. Genetic algorithm- based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, vol. 36, pp. 3066–3076.
  22. Han, J. , Wang, J. , Lu, Y. and Tzvetkov, P. 2002. Mining top-k frequent closed patterns without minimum support. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), pp. 211–218.
  23. Cheung, Y. and Fu, A. 2004. Mining frequent itemsets without support threshold: With and without item constraints. IEEE Transactions on Knowledge and Data Engineering, vol. 16, pp. 1052-1069.
  24. Chai, C. and Li, B. 2010. A novel association rules method based on genetic algorithm and fuzzy set strategy for web mining. Journal of Computers, vol. 5, no. 9, pp. 1448-1455.
  25. Ghosh, A. and Nath, B. 2004. Muti-objective rule mining using genetic algorithms. Information Sciences, vol. 163, pp. 123–133.
  26. Mata, J. , Alvarez, J. L. and Riquelme, J. C. 2002. An evolutionary algorithm to discover numeric association rules. In Proceedings of ACM SAC Symp. Applied Computing, Madrid, Spain, pp. 590–594.
  27. UCI Repository of Machine Learning Databases, Department of Information and Computer Science University of California, 1994. [http://www. ics. uci. edu /~mlearn/MLRepositry. html].
  28. Sewell, M. , Samarabandu, J. , Rodrigo, R. and McIsaac, K. 2006. The rank-scaled mutation rate for genetic algorithms. International Journal of Information Technology, vol. 3, no. 1.
  29. Webb, G. 2000. Efficient search for association rules. In Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 99-107.
  30. Liu, B. , Hsu, W. and Ma, Y. 1998. Integrating classification and association rule mining. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI.
  31. Weka:http://www. cs. waikato. ac. nz/ ml/weka/ index. html.
  32. Aldosari, B. , Almodaifer, G. , Hafez, A. and Mathkour, H. 2012. Constrained association rules for medical data. Journal of Applied Sciences, Asian Networks for Scientific Information, vol. 12, issue 17, pp. 1792-1800.

Keywords

Association Rule, Interestingness Measure, Genetic Operators, Frequent Itemsets