First approach toward on-line evolution of association rules with learning classifier systems

GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Volumn , Issue , 2008, Pages 2031-2038

  Orriols Puig, Albert ^a   Casillas, Jorge ^b   Bernadó Mansilla, Ester ^a  

^a UNIVERSITAT RAMON LLULL   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Association rules; Genetic algorithms; Learning classifier systems; Unsupervised learning

Indexed keywords

ASSOCIATION RULES; CLUSTERING ALGORITHMS; E-LEARNING; GENETIC ALGORITHMS; UNSUPERVISED LEARNING;

APRIORI; CATEGORICAL ATTRIBUTES; CONTINUOUS ATTRIBUTE; INTERESTING RULES; LEARNING CLASSIFIER SYSTEM; MICHIGAN; QUANTITATIVE ASSOCIATION RULES; UNLABELED DATA;

LEARNING SYSTEMS;

EID: 57349125755     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1388969.1389017     Document Type: Conference Paper

References (25)

1. C. Aggarwal. Data streams: Models and algorithms. Springer, 2007.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington D.C., May 1993.
3. 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, VLDB, pages 487-499, Santiago, Chile, September 1994.
4. A. Asuncion and D. J. Newman. UCI Machine Learning Repository: [http://www.ics.uci.edu/~mlearn/MLRepository.html]. University of California, 2007.
5. E. Bernadó-Mansilla and J. Garrell. Accuracy-based Learning Classifier Systems: Models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209-238, 2003.
6. C. H. Cai, A. W.-C. Fu, C. H. Cheng, and W. W. Kwong. Mining association rules with weighted items. In International Database Engineering and Application Symposium, pages 68-77, 1998.
7. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Mining optimized association rules for numeric attributes. In PODS '96: Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, pages 182-191, New York, NY, USA, 1996. ACM.
8. D. E. Goldberg. Genetic algorithms in search, optimization & machine learning. Addison Wesley, 1 edition, 1989.
9. D. E. Goldberg. The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, 1 edition, 2002.
10. T. P. Hong, C. S. Kuo., and S. C. Chi. Trade-off between computation time and number of rules for fuzzy mining from quantitative data. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 9(5):587-604, 2001.
11. M. Houtsma and A. Swami. Set-oriented mining of association rules. Technical Report RJ 9567, Almaden Research Center, San Jose, California, October 1993.
12. M. Kaya and R. Alhajj. Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets and Systems, 152(3):587-601, 2005.
13. B. Lent, A. N. Swami, and J. Widom. Clustering association rules. In Procedings of the IEEE International Conference on Data Engineering, pages 220-231, 1997.
14. J. Mata, J. L. Alvarez, and J. C. Riquelme. An evolutionary algorithm to discover numeric association rules. In SAC '02: Proceedings of the 2002 ACM symposium on Applied computing, pages 590-594, New York, NY, USA, 2002. ACM.
15. R. J. Miller and Y. Yang. Association rules over interval data. In SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pages 452-461, New York, NY, USA, 1997. ACM.
16. M. Núñez, R. Fidalgo, and R. Morales. Learning in environments with unknown dynamics: Towards more robust concept learners. Journal of Machine Learning Research, 8:2595-2628, 2007.
17. A. Orriols-Puig, J. Casillas, and E. Bernadó-Mansilla. Fuzzy-UCS: a michigan-style learning fuzzy-classifier system for supervised learning. IEEE Transactions on Evolutionary Computation, in press.
18. A. Salleb-Aouissi, C. Vrain, and C. Nortet. Quantminer: A genetic algorithm for mining quantitative association rules. In M. M. Veloso, editor, Proceedings of the 2007 International Join Conference on Artificial Intelligence, pages 1035-1040, 2007.
19. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st VLDB Conference, pages 432-443, Zurich, Switzerland, 1995.
20. R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 1-12, Montreal, Quebec, Canada, 1996.
21. C.-Y. Wang, S.-S. Tseng, T.-P. Hong, and Y.-S. Chu. Online generation of association rules under multidimensional consideration based on negative border. Journal of Information Science and Engineering, 23:233-242, 2007.
22. K. Wang, S. H. W. Tay, and B. Liu. Interestingness-based interval merger for numeric association rules. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD, pages 121-128. AAAI Press, 1998.
23. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2): 149-175, 1995.
24. S. W. Wilson. Generalization in the XCS classifier system. In 3rd Annual Conf. on Genetic Programming, pages 665-674. Morgan Kaufmann, 1998.
25. S. W. Wilson. Get real! XCS with continuous-valued inputs. In Learning Classifier Systems. From Foundations to Applications, LNAI, pages 209-219, Berlin, 2000. Springer-Verlag.