Credibilistic fuzzy clustering based on evolutionary method of crazy cats




fuzzy clustering, credibility theory, evolutionary methods of optimization, credibilistic fuzzy clustering, centroids-prototypes, cats swarm, tracing mode, seeking mode, membership level


The problem of fuzzy clustering of large datasets that are sent for processing in both batch and online modes, based on a credibilistic approach, is considered. To find the global extremum of the credibilistic fuzzy clustering goal function, the modification of the swarm algorithm of crazy cats swarms was introduced, that combined the advantages of evolutionary algorithms and a global random search. It is shown that different search modes are generated by a unified mathematical procedure, some cases of which are known algorithms for both local and global optimizations. The proposed approach is easy to implement and is characterized by the high speed and reliability in problems of multi-extreme fuzzy clustering.

Author Biographies

Yevgeniy Bodyanskiy, Kharkiv National University of Radio Electronics, Kharkiv

Yevgeniy V. Bodyanskiy,

Dr. Sci., a professor at the Department of Artificial Intelligence, the scientific head of Control Systems Research Laboratory of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Alina Shafronenko, Kharkiv National University of Radio Electronics, Kharkiv

Alina Yu. Shafronenko,

Candidate of Technical Sciences (Ph.D.), an associate professor at the Department of Informatics of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Iryna Pliss, Kharkiv National University of Radio Electronics, Kharkiv

Iryna P. Pliss,

senior researcher, Candidate of Technical Sciences (Ph.D.), Lеаdіng Rеsеаrсhеr аt Control Systems Research Laboratory of Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.


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Mathematical methods, models, problems and technologies for complex systems research