• DocumentCode
    2657899
  • Title

    Deterministic annealing, constrained clustering, and optimization

  • Author

    Rose, K. ; Gurewitz, E. ; Fox, G.C.

  • Author_Institution
    Caltech Concurrent Comput. Program, California Inst. of Technol., Pasadena, CA, USA
  • fYear
    1991
  • fDate
    18-21 Nov 1991
  • Firstpage
    2515
  • Abstract
    In previous work the authors (Phys. Rev. Let., vol.65, p.945-8, 1990) proposed the concept of deterministic annealing for the problem of clustering and vector quantization. This approach is summarized. The authors extend the clustering method to the constraint clustering method. Adding constraints to the deterministic annealing mechanism expands the variety of optimization problems which can be solved by this method. A brief presentation of the clustering approach is given. Two examples to which the constraint clustering approach can be applied are included
  • Keywords
    neural nets; pattern recognition; simulated annealing; constrained clustering; deterministic annealing; optimization; vector quantization; Clustering algorithms; Clustering methods; Concurrent computing; Constraint optimization; Cost function; Entropy; Probability distribution; Simulated annealing; Stochastic processes; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991. 1991 IEEE International Joint Conference on
  • Print_ISBN
    0-7803-0227-3
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.170767
  • Filename
    170767