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Direct Heuristic Algorithms of Possibilistic Clustering Based on Transitive Approximation of Fuzzy Tolerance




  • Aliaksandr DAMARATSKI



This paper deals with the problem of a heuristic approach to possibilistic clustering. The approach is based on the concept of allotment among fuzzy clusters. The paper provides the description of basic concepts of the heuristic approach to possibilistic clustering. Plans of direct prototype-based heuristic algorithms of possibilistic clustering based on a transitive approximation of a fuzzy tolerance are described in detail. An illustrative example of application of the basic version of the proposed algorithms to Sneath and Sokal's two- dimensional data set is considered. Preliminary conclusions are formulated.

Suggested Citation

  • Dmitri A. VIATTCHENIN & Aliaksandr DAMARATSKI, 2013. "Direct Heuristic Algorithms of Possibilistic Clustering Based on Transitive Approximation of Fuzzy Tolerance," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(3), pages 5-15.
  • Handle: RePEc:aes:infoec:v:17:y:2013:i:3:p:5-15

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    Cited by:

    1. Dmitri A. VIATTCHENIN & Stanislau SHYRAI & Aliaksandr DAMARATSKI, 2014. "Labeling Consequents of Fuzzy Rules Constructed by Using Heuristic Algorithms of Possibilistic Clustering," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 5(3), pages 3-13, December.


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