Abstract
Cluster analysis is one of main methods used in data mining. So far there have existed many cluster analysis approaches such as partitioning method, density-based, k-means, k-nearest neighborhood, etc. Recently, some researchers have explored a few kernel-based clustering methods, e.g., kernel-based K-means clustering. The new algorithms have demonstrated some advantages. So it’s needed to explore the basic principle underlain the algorithms such as whether the kernel function transformation can increase the separability of the input data in clustering and how to use the principle to construct new clustering methods. In this paper, we will discuss the problems.
Supported by National Nature Science Foundation of China (Grant No. 60135010), Chinese National Key Foundation Research Plan (2004CB318108) and Innovative Research Team of 211 Project in Anhui University.
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Zhang, L., Wu, T., Zhang, Y. (2005). A Kernel Function Method in Clustering. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_6
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DOI: https://doi.org/10.1007/11430919_6
Publisher Name: Springer, Berlin, Heidelberg
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