Abstract
The clustering method based on one-class support vector machine has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random datasets are employed for its initialization. In this paper, a novel initialization method based on the core clusters is used for the clustering algorithm based one-class support vector machine. The core clusters are gained by constructing the neighborhood graph and they are regarded as the initial datasets of the clustering algorithm based one-class support vector machine. To investigate the effectiveness of the proposed approach, several experiments are done on four datasets. Experimental results show that the new presented method can improve the clustering performance compared to the previous clustering algorithm based on one-class support vector machine and k-means approach.
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References
Fillippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41(1), 176–190 (2008)
Jain, A.K., Murty, M.N., Flyn, P.J.: Data clustering: a review. ACM Computing Surveys 32(3), 256–323 (1999)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Tou, J.T., Gonzalez, R.C.: Pattern recognition principles. Addison-Wesley, London (1974)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: A kernel-based subtractive clustering method. Pattern Recognition Letters 26(7), 879–891 (2005)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: Neural-gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 801–805 (2005)
Ormella, C., Anastasios, M., Sandhya, S., Don, K., Sijia, L., Philip, K.M., Radek, E.: DifFUZZY: a fuzzy clustering algorithm for complex datasets. International Journal of Computational Intelligence in Bioinformatics and Systems Biology 1(4), 402–417 (2010)
Gu, L., Sun, F.C.: Two novel kernel-based semi-supervised clustering methods by seeding. In: Proceedings of the 2009 Chinese Conference on Pattern Recognition (2009)
Wolfe, P.: A duality theorem for nonlinear programming. Q. Appl. Math. (19), 239–244 (1961)
Kukn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 481–492 (1951)
Bicego, M., Figueiredo, M.A.T.: Soft clustering using weighted one-class support vector machines. Pattern Recognition 42(1), 27–32 (2009)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLSummary.html
Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. Journal of Cybernet. (3), 32–57 (1974)
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Gu, L. (2013). Use the Core Clusters for the Initialization of the Clustering Based on One-Class Support Vector Machine. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_10
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DOI: https://doi.org/10.1007/978-3-642-38715-9_10
Publisher Name: Springer, Berlin, Heidelberg
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