Abstract
For the problem of cluster analysis, the objective function based algorithms are popular and widely used methods. However, the performance of these algorithms depends upon the priori information about cluster number and cluster prototypes. Moreover, it is only effective for analyzing data set with the same type of cluster prototypes. For this end, this paper presents a novel algorithm based on support vector machine (SVM) for realizing fully unsupervised clustering. The experimental results with various test data sets illustrate the effectiveness of the proposed novel clustering algorithm based on SVM.
This project was supported by NFSC (No.60202004) and the key project of Chinese Ministry of Education (No.104173).
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References
He, Q.: Advance of the Theory and Application of Fuzzy Clustering Analysis. Fuzzy System and Fuzzy Mathematics 12, 89–94 (1998)
Gao, X.: Studies of Optimization and Applications of Fuzzy Clustering Algorithm, Doctoral Dissertation, Xidian University, Xi’an, China (1999)
Li, J., Gao, X., Jiao, L.: A Novel Clustering Algorithm Based on Immune Network with Limited Resource. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 319–331. Springer, Heidelberg (2004)
Scholkopf, R.C., Williamson, B.: Support Vector Method for Novelty Detection. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems 12: Proc. of the 1999 Conf. (2000)
Tax, D.M.J., Duin, R.P.W.: Support Vector Domain Description. Pattern Recognition Letters 20, 1991–(1999)
Fletcher, R.: Practical Methods of Optimization. Wiley Interscience, Chichester (1987)
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Li, J., Gao, X., Jiao, L. (2005). A Novel Clustering Method Based on SVM. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_10
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DOI: https://doi.org/10.1007/11427445_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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