Abstract
This paper presents a cluster validity measure with outlier detection for support vector clustering (SVC) algorithm. We proposed an outlier detection approach for dealing with noise data and a cluster validity process for identifying an optimal cluster configuration with suitable parameters without a priori knowledge regarding the given data sets. Since SVC is a kernel-based clustering approach, the parameter of kernel functions and the soft-margin constants in Lagrangian functions play a crucial role in the clustering results. A validity measure with outlier detection has been developed to automatically determine suitable parameters. Using these parameters, the SVC algorithm can identify an optimal cluster number and increase its robustness to outliers and noise. The simulations have been conducted to demonstrate the effectiveness of the proposed cluster validity measure and outlier detection for benchmark datasets.
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References
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. J. Machine Learning Research 2, 125–137 (2001)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: A Support Vector Clustering Method. In: Proc. Int. Conf. on Pattern Recognition, vol. 2, pp. 724–727 (2000)
Boser, B., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. In: Proc. Fifth Annu. Workshop on Computational Learning Theory, vol. 5, pp. 144–152 (1992)
Cortes, C., Vapnik, V.: Support-Vector Network. Machine Learning 20, 273–297 (1995)
Chiang, J.-H., Hao, P.-Y.: A New Kernel-Based Fuzzy Clustering Approach: Support Vector Clustering with Cell Growing. IEEE Trans. Fuzzy Systems 11, 518–527 (2003)
Bezdek, J.C.: Numerical Taxonomy with Fuzzy Sets. J. Math. Biology 1, 57–71 (1974)
Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separation Clusters. J. Cybernetics 3, 32–57 (1974)
Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Trans. Pattern Analysis and Machine Intelligence 1, 224–227 (1979)
Bezdek, J.C.: Cluster Validity with Fuzzy Sets. J. Cybernetics 3, 58–72 (1974)
Bolshakova, N., Azuaje, F.: Cluster Validation Techniques for Genome Expression Data. Signal Processing 83, 825–833 (2003)
Rousseeuw, P.J.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)
Xie, X.-L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 13, 841–847 (1991)
Bezdek, J.C., Pal, N.R.: Some New Indexes of Cluster Validity. IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics 28, 301–315 (1998)
Chou, C.-H., Su, M.-C., Lai, E.: A New Cluster Validity Measure for Clusters with Different Densities. In: IASTED Int. Conf. on Intelligent Systems and Control, pp. 276–281 (2003)
Fisher, R.: The Use of Multiple Measurements in Taxonomic Problems. Ann. Eugenics 7, 179–188 (1936)
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Wang, JS., Chiang, JC., Yang, YT. (2007). Support Vector Clustering with Outlier Detection. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_48
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DOI: https://doi.org/10.1007/978-3-540-74282-1_48
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