Open AccessONLINE RELIABLE FUZZY DATA CLUSTERING USING A SPECIAL TYPE ACCESSORY FUNCTION
Author(s) -
E.V. Bodyansky,
Аліна Шафроненко,
І. М. Klimova
Publication year - 2019
Publication title -
bionics of intelligence
Language(s) - English
Resource type - Journals
eISSN - 2663-306X
pISSN - 2663-3051
DOI - 10.30837/bi.2019.2(93).01
Subject(s) - cluster analysis , fuzzy clustering , data mining , centroid , membership function , cauchy distribution , artificial intelligence , function (biology) , feature (linguistics) , computer science , pattern recognition (psychology) , fuzzy logic , flame clustering , mathematics , fuzzy set , cure data clustering algorithm , statistics , linguistics , philosophy , evolutionary biology , biology
An online method of reliable fuzzy clustering is proposed, designed to analyze data sequentially received for processing. A feature of the developed approach is the use of the membership function of a special kind described by the density function of the Cauchy distribution. The actual procedure for clarifying the centroids of clusters is essentially a self-learning rule “The Winner Takes More” (WTM), in which the neighborhood function is generated by the introduced membership function.
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