Evaluation of the performance of clustering algorithms in kernel-induced feature space

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By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However. few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2005-04
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.38, pp.607 - 611

ISSN
0031-3203
DOI
10.1016/j.patcog.2004.09.006
URI
http://hdl.handle.net/10203/18799
Appears in Collection
BiS-Journal Papers(저널논문)
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