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Dynamic Dissimilarity Measure for Support-Based Clustering

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84040

Lee,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Lee, D. (2010). Dynamic Dissimilarity Measure for Support-Based Clustering. IEEE Transactions on Knowledge and Data Engineering, 22(6), 900-905. doi:10.1109/TKDE.2009.140.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BF70-E
Abstract
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.