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A new competitive learning algorithm for data clustering

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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


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