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Инд. авторы:	Berikov V., Pestunov I.
Заглавие:	Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties
Библ. ссылка:	Berikov V., Pestunov I. Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties // Pattern Recognition. - 2017. - Vol.63. - P.427-436. - ISSN 0031-3203.
Внешние системы:	DOI: 10.1016/j.patcog.2016.10.017; РИНЦ: 29472082; SCOPUS: 2-s2.0-84998679702; WoS: 000389785900034;
Реферат:	eng: We consider an approach to ensemble clustering based on weighted co-association matrices, where the weights are determined with some evaluation functions. Using a latent variable model of clustering ensemble, it is proved that, under certain assumptions, the clustering quality is improved with an increase in the ensemble size and the expectation of evaluation function. Analytical dependencies between the ensemble size and quality estimates are derived. Theoretical results are supported with numerical examples using Monte-Carlo modeling and segmentation of a real hyperspectral image under presence of noise channels. © 2016 Elsevier Ltd
Ключевые слова:	Clustering Ensemble; Co-association matrix; Ensemble size; Error bound; Latent variable modeling; Cobalt compounds; Hyper-spectral images; Cluster validity index; Spectroscopy; Quality control; Image segmentation; Function evaluation; Cobalt; Weighted clustering ensemble; Latent variable model; Hyperspectral image segmentation; Error bound; Ensemble size; Cluster validity indices; Co-association matrix;
Издано:	2017
Физ. характеристика:	с.427-436
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