Association rule mining using a multi-objective grammar-based ant programming algorithm

International Conference on Intelligent Systems Design and Applications, ISDA

Volumn , Issue , 2011, Pages 971-977

  Olmo, Juan Luis ^a   Luna, José María ^a   Romero, José Raúl ^a   Ventura, Sebastián ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

ant colony optimization (ACO); ant programming (AP); Association rule mining (ARM); data mining (DM)

Indexed keywords

ANT PROGRAMMING (AP); ANT-COLONY OPTIMIZATION; DATA SETS; EXHAUSTIVE SEARCH; MULTI OBJECTIVE; PROGRAMMING ALGORITHMS; RULE MINING ALGORITHMS; SEARCH SPACES; SUPPORT AND CONFIDENCE;

DATA MINING; GENETIC PROGRAMMING; INTELLIGENT SYSTEMS; SYSTEMS ANALYSIS;

ALGORITHMS;

EID: 84857623603     PISSN: 21647143     EISSN: 21647151     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2011.6121784     Document Type: Conference Paper

References (22)

1. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases," in Proceedings of the 20th International Conference on Very Large Data Bases, ser. VLDB '94. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994, pp. 487-499. [Online]. Available: http://portal.acm.org/citation.cfm?id=645920.672836
2. J. Han, J. Pei, Y. Yin, and R. Mao, "Mining frequent patterns without candidate generation: A frequent-pattern tree approach," Data Min. Knowl. Discov., vol. 8, pp. 53-87, January 2004. [Online]. Available: http://dx.doi.org/10.1023/ B:DAMI.0000005258.31418.83
3. J. M. Luna, J. R. Romero, and S. Ventura, "Analysis of the effectiveness of G3PARM algorithm," in HAIS, ser. LNCS. Springer Berlin / Heidelberg, 2010, vol. 6077, pp. 27-34. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-642-13803-4
4. X. Yan, C. Zhang, and S. Zhang, "Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support," Expert Syst. Appl., vol. 36, pp. 3066-3076, March 2009. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2008.01.028
5. R. J. Kuo, C. M. Chao, and Y. T. Chiu, "Application of particle swarm optimization to association rule mining," Appl. Soft Comput., vol. 11, pp. 326-336, January 2011. [Online]. Available: http://dx.doi.org/10.1016/j. asoc.2009.11.023
6. M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: Optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics-Part B, vol. 26, pp. 29-41, 1996. (Pubitemid 126780573)
7. D. Martens, M. De Backer, J. Vanthienen, M. Snoeck, and B. Baesens, "Classification with ant colony optimization," IEEE Transactions on Evolutionary Computation, vol. 11, pp. 651-665, 2007.
8. R. Parpinelli, A. A. Freitas, and H. S. Lopes, "Data mining with an ant colony optimization algorithm," IEEE Trans on Evolutionary Computation, vol. 6, pp. 321-332, 2002. (Pubitemid 35032734)
9. R. J. Kuo and C. W. Shih, "Association rule mining through the ant colony system for national health insurance research database in Taiwan," Comput. Math. Appl., vol. 54, pp. 1303-1318, 2007. [Online]. Available: http: //dx.doi.org/10.1016/j.camwa.2006.03.043
10. H. A. Abbass, X. Hoai, and R. I. Mckay, "AntTAG: A new method to compose computer programs using colonies of ants," in In The IEEE Congress on Evolutionary Computation, 2002, pp. 1654-1659.
11. M. Boryczka and Z. J. Czech, "Solving approximation problems by ant colony programming," in GECCO Late Breaking Papers, 2002, pp. 39-46.
12. J. L. Olmo, J. R. Romero, and S. Ventura, "A grammar based ant programming algorithm for mining classification rules," in 2010 IEEE Congress on Evolutionary Computation (CEC), 2010, pp. 225-232. [Online]. Available: http: //dx.doi.org/10.1109/CEC.2010.5586492
13. J. R. Koza, Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: The MIT Press, 1992.
14. J. M. Luna, J. R. Romero, and S. Ventura, "Design and behaviour study of a grammar guided genetic programming algorithm for mining association rules," Knowledge and Information Systems, pp. 1-21 (In Press), 2011.
15. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Multiobjective association rule mining," in Proceedings of the Multiobjective Problem Solving from Nature, Reykjavik, Iceland, September 2006.
16. J. L. Olmo, J. R. Romero, and S. Ventura, "Using ant programming guided by grammar for building rule-based classifiers," IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics (In Press), pp. 1-15, 2011. [Online]. Available: http://dx.doi.org/10.1109/TSMCB. 2011.2157681
17. R. J. Mullen, D. Monekosso, S. Barman, and P. Remagnino, "A review of ant algorithms," Expert Systems with Applications, vol. 36, pp. 9608-9617, 2009. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2009.01. 020
18. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: Nsga-ii," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2002. (Pubitemid 34555372)
19. A. Geyer-Schulz, Fuzzy Rule-Based Expert Systems and Genetic Machine Learning, ser. Studies in Fuzziness. Heidelberg: Physica-Verlag, 1995, vol. 3.
20. U. M. Fayyad and K. B. Irani, "Multi-interval discretization of continuous-valued attributes for classification learning," in 13th International Joint Conference on Uncertainly in Artificial Intelligence(IJCAI93), 1993, pp. 1022-1029.
21. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The weka data mining software: an update," SIGKDD Explor. Newsl., vol. 11, pp. 10-18, November 2009.
22. J. Demšar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learn. Res., vol. 7, pp. 1-30, 2006. (Pubitemid 43022939)