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Dynamic Dissimilarity Measure for Support-Based Clustering




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.

Author(s): Lee, D. and Lee, J.
Journal: IEEE Transactions on Knowledge and Data Engineering
Volume: 22
Number (issue): 6
Pages: 900-905
Year: 2010
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/TKDE.2009.140
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Dynamic Dissimilarity Measure for Support-Based Clustering},
  author = {Lee, D. and Lee, J.},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  volume = {22},
  number = {6},
  pages = {900-905},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2010},
  month_numeric = {6}