Online algorithm for possibilitic fuzzy clustering based on evolutionary cat swarm optimization

Публікація:
Online algorithm for possibilitic fuzzy clustering based on evolutionary cat swarm optimization

dc.contributor.author	Bodyanskiy, Ye. V.
dc.contributor.author	Shafronenko, A. Yu.
dc.date.accessioned	2020-11-13T19:48:17Z
dc.date.available	2020-11-13T19:48:17Z
dc.date.issued	2019
dc.description.abstract	The problem of clustering of multidimensional observations is often found in many applications related to data mining and exploratory data analysis. The traditional approach to solving these problems requires that every observation could belong to only one cluster at a more natural is situations when a feature vector with the various possible levels of memberships can belong to multiple classes. This situation is the subject of fuzzy cluster analysis, rapidly developing now. We propose online adaptive approach for this task solving.	uk_UA
dc.identifier.citation	Ye. Bodyanskiy, A. Shafronenko Online algorithm for Рossibilitic fuzzy clustering based on evolutionary cat swarm op-timization. Science and Education a New Dimension. Natural and Tech-nical Sciences - VII(23), Issue: 193, 2019 - P. 86-88	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/13787
dc.language.iso	en	uk_UA
dc.subject	Fuzzy clustering	uk_UA
dc.subject	learning rule	uk_UA
dc.subject	cat swarm optimization	uk_UA
dc.subject	tracing mode	uk_UA
dc.subject	seeking mode	uk_UA
dc.title	Online algorithm for possibilitic fuzzy clustering based on evolutionary cat swarm optimization	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication

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