Abstract
The Self-Splitting Competitive Learning (SSCL) is a powerful algorithm that solves the difficult problems of determining the number of clusters and the sensitivity to prototype initialization in clustering. The SSCL algorithm iteratively partitions the data space into natural clusters without a prior information on the number of clusters. However, SSCL suffers from two major disadvantages: it does not have a proven convergence and the speed of learning process is slow. We propose solutions for these two problems. Firstly, we introduce a new update scheme and lead a proven convergence of Asymptotic Property Vector. Secondly, we modify the split-validity to accelerate the learning process. Experiments show these techniques make the algorithm faster than the original one.
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References
Barlow, H.B.: Unsupervised learning. Neural Computation 1, 295–311 (1989)
Bischof, H., Leonardis̈, A., Selb, A.: MDL principle for robust vector quantization. Pattern Analysis and Applications 2, 59–72 (1999)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symposium on Mathematics, Statistics and Probability, pp. 282–297. Univ. California Press, Berkeley (1967)
Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International conference on Machine Learning, pp. 727–734. Morgan Kaufmann, San Francisco (2000)
Pelleg, D., Moore, A.: Accelerating exact k-means with geometric reasoning. Technical report, Carnegie Mellon University, Pittsburgh, PA (2000)
Zhang, Y.J., Liu, Z.Q.: Self-splitting competitive learning: A new on-line clusteringparadigm. IEEE Trans. on Neural Networks 13, 369–380 (2002)
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Liu, J., Ramamohanarao, K. (2005). Improved Self-splitting Competitive Learning Algorithm. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_44
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DOI: https://doi.org/10.1007/11430919_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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