Abstract
Classical fuzzy c-means and its variants cannot get better effect when the characteristic of samples is not obvious, and these algorithms run easily into locally optimal solution. According to the drawbacks, a novel mercer kernel based fuzzy clustering self-adaptive algorithm(KFCSA) is presented. Mercer kernel method is used to map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. A self-adaptive algorithm is proposed to decide the number of clusters, which is not given in advance, and it can be gotten automatically by a validity measure function. In addition, attribute reduction algorithm is used to decrease the numbers of attributes before high dimensional data are clustered. Finally, experiments indicate that KFCSA may get better performance.
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References
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symposium, pp. 281–297 (1967)
Hartigan, J., Wang, M.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York (1981)
Jain, A., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)
Wallace, R.: Finding natural clusters through entropy minimization. Ph.D Thesis. CarnegieMellon University, CS Dept. (1989)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Girolami, M.: Mercer kernel based clustering in feature space. IEEE Trans Neural Network 13(3), 780–784 (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, K., Liu, Y. (2005). KFCSA: A Novel Clustering Algorithm for High-Dimension Data. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_67
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DOI: https://doi.org/10.1007/11539506_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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