Abstract
Recently, an online agglomerative clustering method called AddC (I. D. Guedalia et al. Neural Comput. {\bf 11} (1999), 521--540) was proposed for nonstationary data clustering. Although AddC possesses many good attributes, a vital problem of that method is its sensitivity to noises, which limits its use in real-word applications. In this paper, based on \hbox{kernel-induced} distance measures, a robust online clustering (ROC) algorithm is proposed to remedy the problem of AddC. Experimental results on artificial and benchmark data sets show that ROC has better clustering performances than AddC, while still inheriting advantages such as clustering data in a single pass and without knowing the exact number of clusters beforehand.
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References
N Cristianini J. Taylor (2000) An Introduction to SVMs and Other Kernel-based Learning Methods Cambridge University Press Cambridge
D. Deng N. Kasabov (2003) ArticleTitleOn-line pattern analysis by evolving self-organizing maps Neurocomputing 51 87–103 Occurrence Handle10.1016/S0925-2312(02)00599-4
I. D. Guedalia M. London M. Werman (1999) ArticleTitleAn on-line agglomerative clustering method for nonstationary data Neural Computation 11 521–540
P. J. Huber (Eds) (1981) Robust Statistics New York Wiley
A. K Jain R.C. Dubes (1988) Algorithms for Clustering Data Prentice Hall Englewood Cliffs, NJ
B. Schölkopf A. Smola K.R. Müller (1998) ArticleTitleNonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 IssueID5 1299–1319
N. Ueda R. Nakano (1994) ArticleTitleA new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers Neural Networks 7 IssueID8 1211–1227
D. Zhang S. Chen (2003) ArticleTitleClustering incomplete data using kernel-based fuzzy c-means algorithm Neural Processing Letters 18 155–162
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Zhang, D., Zhang, D., Chen, S. et al. Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures. Neural Process Lett 21, 45–51 (2005). https://doi.org/10.1007/s11063-004-2793-y
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DOI: https://doi.org/10.1007/s11063-004-2793-y