A robust deterministic annealing algorithm for data clustering

  title={A robust deterministic annealing algorithm for data clustering},
  author={XuLei Yang and Qing Song and Yi-Lei Wu},
  journal={Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.},
  pages={1878-1882 vol. 3}
In this paper, a new robust deterministic annealing (RDA) clustering algorithm is proposed. This method takes advantages of conventional noise clustering (NC) and deterministic annealing (DA) algorithms in terms of independence of data initialization, ability to avoid poor local optima, better performance for unbalanced data, and robustness against noise. The superiority of the proposed RDA clustering algorithm is supported by simulation results. 
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A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-Separated Cluster

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