Online Recurrent Me-thod Of Credibilistic Fuzzy Clusterin

Публікація:
Online Recurrent Me-thod Of Credibilistic Fuzzy Clusterin

dc.contributor.author	Bodyanskiy, Ye. V.
dc.contributor.author	Shafronenko, A. Yu.
dc.contributor.author	Rudenko, D. A.
dc.contributor.author	Klymova, I. N.
dc.date.accessioned	2020-11-14T14:07:35Z
dc.date.available	2020-11-14T14:07:35Z
dc.date.issued	2020
dc.description.abstract	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.	uk_UA
dc.identifier.citation	Ye. Bodyanskiy, A. Shafronenko, D. Rudenko, I. Klymova Online Recurrent Me-thod Of Credibilistic Fuzzy Clustering. 5th International scientific and practical conference “Topical of the development of modern science” (January 15-17, 2020), Sofia, Bulgaria, P. 37-40	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/13805
dc.language.iso	en	uk_UA
dc.subject	Fuzzy clustering	uk_UA
dc.subject	processing	uk_UA
dc.subject	gradient optimization	uk_UA
dc.subject	membership level	uk_UA
dc.subject	online mode	uk_UA
dc.title	Online Recurrent Me-thod Of Credibilistic Fuzzy Clusterin	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication

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