Summary
This chapter presents a novel approach to clustering using an accuracy-based Learning Classifier System. Our approach achieves this by exploiting the generalization mechanisms inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of synthetic datasets.
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References
Booker, L.B. (1989) Triggered Rule Discovery in Classifier Systems. In J.D. Schaffer (ed) Proceeding of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, CA, pp. 265–274
Bull, L. (ed) (2004) Applications of Learning Classifier Systems. Springer, Berlin Heidelberg New York
Bull, L. (2005) Two Simple Learning Classifier Systems. In L. Bull & T. Kovacs (eds) Foundations of Learning Classifier Systems. Springer, Berlin Heidelberg New York, pp. 63–90
Butz, M. & Wilson, S. (2001) An Algorithmic Description of XCS. In P.L. Lanzi, W. Stolzmann, & S.W. Wilson (eds) Advances in Learning Classifier Systems. Third International Workshop (IWLCS-2000), Lecture Notes in Artificial Intelligence (LNAI-1996). Springer, Berlin Heidelberg New York
Butz, M., Kovacs, T., Lanzi, P.-L., & Wilson, S.W. (2004) Toward a Theory of Generalization and Learning in XCS. IEEE Transactions on Evolutionary Computation 8(1): 28–46
Davies, D.L. & Bouldin, D.W. (1979) A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2): 224–227
Dixon, P., Corne, D., & Oates, M. (2003) A Ruleset Reduction Algorithm for the XCS Learning Classifier System. In P.L. Lanzi, W. Stolzmann, & S. Wilson (eds) Proceedings of the 5th International Workshop on Learning Classifier Systems. Springer, Berlin Heidelberg New York, pp. 20–29
Fu, C. & Davis, L. (2002) A Modified Classifier System Compaction Algorithm. In Banzhaff et al. (eds) Proceedings of GECCO 2002. Morgan Kaufmann, San Francisco, CA, pp. 920–925
Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press
Holland, J.H. (1976) Adaptation. In R. Rosen & F.M. Snell (eds) Progress in Theoretical Biology, vol. 4. Plenum, New York, pp. 263–293
Maulik, U. & Bandyopadhyay, S. (2000) Genetic Algorithm-Based Clustering Technique. Pattern Recognition 33: 1455–1465
O’Hara, T. & Bull, L. (2005) A Memetic Accuracy-based Neural Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. , pp. 2040–2045
Sarafis, I.A., Trinder, P.W., & Zalzala, A.M.S. (2003) Mining Comprehensible Clustering Rules with an Evolutionary Algorithm. In E. Cant’u- Paz et al. (eds) Proceedings of Genetic and Evolutionary Computation Conference (Gecco’03), LNCS 2724, pp. 2301–2312
Stone, C. & Bull, L. (2003) For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation 11(3): 299–336
Tamee, K., Bull, L., & Pinngern, O. (2006) A Learning Classifier System Approach to Clustering. In Sixth International Conference on Intelligent System Design and Application (ISDA), Jinan, China. IEEE, New York, vol. ISDA I, pp. 621–626
Tibshirani, R., Walther, G., & Hastie, T. (2000) Estimating the Number of Clusters in a Dataset via the Gap Statistic. Journal of the Royal Statistical Society B 63: 411–423
Tseng, L.Y. & Yang, S.B. (2001) A Genetic Approach to the Automatic Clustering Problem. Pattern Recognition 34: 415–424
Wilson, S.W. (1995) Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2): 149–176
Wilson, S.W. (2000) Get Real! XCS with Continuous-Valued Inputs. In P.L. Lanzi, W. Stolzmann, & S.W. Wilson (eds) Learning Classifier Systems From Foundations to Applications. Springer, Berlin Heidelberg New York, pp. 209–219
Wilson, S. (2002). Compact Rulesets from XCSI. In P.L. Lanzi, W. Stolzmann, & S.W. Wilson (eds) Proceedings of the 4th International Workshop on Learning Classifier Systems. Springer, Berlin Heidelberg New York, pp. 197–210
Wyatt, D. & Bull, L. (2004) A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces. In N. Krasnagor, W. Hart, & J. Smith (eds) Recent Advances in Memetic Algorithms. Springer, Berlin Heidelberg New York, pp. 355–396
Wyatt, D., Bull, L., & Parmee, I. (2004) Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System. In I. Parmee (ed) Adaptive Computing in Design and Manufacture VI. Springer, Berlin Heidelberg New York, pp. 235–248
Xu, R. & Winch, D. (2005) Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3): 645–678
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Tamee, K., Bull, L., Pinngern, O. (2008). Towards Clustering with Learning Classifier Systems. In: Bull, L., Bernadó-Mansilla, E., Holmes, J. (eds) Learning Classifier Systems in Data Mining. Studies in Computational Intelligence, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78979-6_9
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DOI: https://doi.org/10.1007/978-3-540-78979-6_9
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