A new competitive learning algorithm for data clustering

dc.contributor.author	BOTOCA, Corina
dc.contributor.author	BUDURA, Georgeta
dc.contributor.author	MICLAU, Nicolae
dc.date.accessioned	2019-10-29T07:27:12Z
dc.date.available	2019-10-29T07:27:12Z
dc.date.issued	2005
dc.identifier.citation	BOTOCA, Corina, BUDURA, Georgeta, MICLAU, Nicolae. A new competitive learning algorithm for data clustering. In: Microelectronics and Computer Science: proc. of the 4th intern. conf., September 15-17, 2005. Chişinău, 2005, vol. 2, pp. 75-78. ISBN 9975-66-038-X.	en_US
dc.identifier.isbn	9975-66-038-X
dc.identifier.uri	http://repository.utm.md/handle/5014/5529
dc.description.abstract	This paper presents a new competitive learning algorithm for data clustering, named the dynamically penalized rival competitive learning algorithm (DPRCA). It is a variant of the rival penalized competitive algorithm and it performs appropriate clustering without knowing the clusters number, by automatically driving extra seed points far away from the input data set. It doesn’t have the "dead neurons" problem. The performances of the DPRCA algorithm were tested by simulations carried out considering different conditions of noise.	en_US
dc.language.iso	en	en_US
dc.publisher	Technical University of Moldova	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	learning algorithms	en_US
dc.subject	neural networks	en_US
dc.title	A new competitive learning algorithm for data clustering	en_US
dc.type	Article	en_US