Abstract
The qualities of clustering, including those obtained by the kernel-based methods should be assessed. In this paper, by investigating the inherent pairwise similarities in kernel matrix implicitly defined by the kernel function, we define two statistical similarity coefficients which can be used to describe the within-cluster and between-cluster similarities between the data items, respectively. And then, an efficient cluster validity index and a self-adaptive kernel clustering (SAKC) algorithm are proposed based on these two similarity coefficients. The performance and effectiveness of the proposed validity index and SAKC algorithm are demonstrated, compared with some existing methods, on two synthetic datasets and four UCI real databases. And the robustness of this new index with Gaussian kernel width is also explored tentatively.
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© 2006 Springer-Verlag Berlin Heidelberg
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Pu, YW., Zhu, M., Jin, WD., Hu, LZ. (2006). An Efficient Similarity-Based Validity Index for Kernel Clustering Algorithm. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_153
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DOI: https://doi.org/10.1007/11759966_153
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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