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Evolution of Interesting Association Rules Online with Learning Classifier Systems

  • Conference paper
Learning Classifier Systems (IWLCS 2009, IWLCS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6471))

Abstract

This paper presents CSar, a Michigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled data quickly and efficiently. The results provided in this paper show that CSar is able to evolve interesting rules on problems that consist of both categorical and continuous attributes. Moreover, the comparison of CSar with Apriori on a problem that consists only of categorical attributes highlights the competitiveness of CSar with respect to more specific learners that perform enumeration to return all possible association rules. These promising results encourage us to further investigate on CSar.

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References

  1. Aggarwal, C. (ed.): Data streams: Models and algorithms. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington D.C, pp. 207–216 (May 1993)

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499 (September 1994)

    Google Scholar 

  4. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

  5. Bacardit, J., Krasnogor, N.: Fast rule representation for continuous attributes in genetics-based machine learning. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1421–1422. ACM, New York (2008)

    Google Scholar 

  6. Bernadó-Mansilla, E., Garrell, J.M.: Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation 11(3), 209–238 (2003)

    Article  Google Scholar 

  7. Bernadó-Mansilla, E., Llorà, X., Garrell, J.M.: XCS and GALE: A comparative study of two learning classifier systems on data mining. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 115–132. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Cai, C.H., Fu, A.W.-C., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: International Database Engineering and Application Symposium, pp. 68–77 (1998)

    Google Scholar 

  9. Divina, F.: Hybrid Genetic Relational Search for Inductive Learning. PhD thesis, Department of Computer Science, Vrije Universiteit, Amsterdam, the Netherlands (2004)

    Google Scholar 

  10. Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Mining optimized association rules for numeric attributes. In: PODS 1996: Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 182–191. ACM, New York (1996)

    Chapter  Google Scholar 

  11. Goldberg, D.E.: Genetic algorithms in search, optimization & machine learning, 1st edn. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  12. Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press (1975)

    Google Scholar 

  13. Hong, T.P., Kuo, C.S., Chi, S.C.: 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)

    Article  MATH  Google Scholar 

  14. Houtsma, M., Swami, A.: Set-oriented mining of association rules. Technical Report RJ 9567, Almaden Research Center, San Jose, California (October 1993)

    Google Scholar 

  15. Kaya, M., Alhajj, R.: Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets and Systems 152(3), 587–601 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Lent, B., Swami, A.N., Widom, J.: Clustering association rules. In: Procedings of the IEEE International Conference on Data Engineering, pp. 220–231 (1997)

    Google Scholar 

  17. Mata, J., Alvarez, J.L., Riquelme, J.C.: An evolutionary algorithm to discover numeric association rules. In: SAC 2002: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 590–594. ACM, New York (2002)

    Google Scholar 

  18. Miller, R.J., Yang, Y.: Association rules over interval data. In: SIGMOD 1997: Proceedings of the 1997 ACM SIGMOD International Conference on Management of data, pp. 452–461. ACM, New York (1997)

    Chapter  Google Scholar 

  19. Núñez, M., Fidalgo, R., Morales, R.: Learning in environments with unknown dynamics: Towards more robust concept learners. Journal of Machine Learning Research 8, 2595–2628 (2007)

    MathSciNet  MATH  Google Scholar 

  20. Salleb-Aouissi, A., Vrain, C., Nortet, C.: Quantminer: A genetic algorithm for mining quantitative association rules. In: Veloso, M.M. (ed.) Proceedings of the 2007 International Join Conference on Artificial Intelligence, pp. 1035–1040 (2007)

    Google Scholar 

  21. Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st VLDB Conference, Zurich, Switzerland, pp. 432–443 (1995)

    Google Scholar 

  22. Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, pp. 1–12 (1996)

    Google Scholar 

  23. Wang, C.-Y., Tseng, S.-S., Hong, T.-P., Chu, Y.-S.: Online generation of association rules under multidimensional consideration based on negative border. Journal of Information Science and Engineering 23, 233–242 (2007)

    Google Scholar 

  24. Wang, K., Tay, S.H.W., Liu, B.: Interestingness-based interval merger for numeric association rules. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD, pp. 121–128. AAAI Press, Menlo Park (1998)

    Google Scholar 

  25. Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)

    Article  Google Scholar 

  26. Wilson, S.W.: Generalization in the XCS classifier system. In: 3rd Annual Conf. on Genetic Programming, pp. 665–674. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  27. Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209–219. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Grup de Recerca en Sistemes Intel.ligents, La Salle - Universitat Ramon Llull, Quatre Camins 2, 08022, Barcelona, Spain

    Albert Orriols-Puig

  2. Dept. Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Jorge Casillas

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  1. Albert Orriols-Puig
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  2. Jorge Casillas
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Editor information

Editors and Affiliations

  1. School of Computer Science, ASAP research group, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, LE12 5RD, Sutton Bonington, UK

    Jaume Bacardit

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140, Wellington, New Zealand

    Will Browne

  3. Department of Brain and Cognitive Sciences, University of Rochester, Meliora Hall, 14627, Rochester, NY, USA

    Jan Drugowitsch

  4. Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins, 2, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  5. Department of Psychology III, University of Würzburg, COBOSLAB – Cognitive Bodyspaces: Learning and Behavior,, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

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Orriols-Puig, A., Casillas, J. (2010). Evolution of Interesting Association Rules Online with Learning Classifier Systems. In: Bacardit, J., Browne, W., Drugowitsch, J., Bernadó-Mansilla, E., Butz, M.V. (eds) Learning Classifier Systems. IWLCS IWLCS 2009 2008. Lecture Notes in Computer Science(), vol 6471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17508-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-17508-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17507-7

  • Online ISBN: 978-3-642-17508-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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