Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties

Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties Full article

Journal

Pattern Recognition
ISSN: 0031-3203

Output data

Year: 2017, Volume: 63, Pages: 427-436 Pages count : 10 DOI: 10.1016/j.patcog.2016.10.017

Tags

Cluster validity index; Co-association matrix; Ensemble size; Error bound; Hyperspectral image segmentation; Latent variable model; Weighted clustering ensemble

Authors

Berikov V. ^1,2 , Pestunov I. ^2,3

Affiliations

1	Sobolev Institute of Mathematics SB RAS, Novosibirsk, 630090, Russian Federation
2	Novosibirsk State University, Novosibirsk, 630090, Russian Federation
3	Institute of Computational Technologies SB RAS, Novosibirsk, 630090, Russian Federation

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

Berikov V. , Pestunov I.
Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties
Pattern Recognition. 2017. V.63. P.427-436. DOI: 10.1016/j.patcog.2016.10.017 WOS Scopus OpenAlex

Web of science:	WOS:000389785900034
Scopus:	2-s2.0-84998679702
OpenAlex:	W2538920018

DB	Citing
Scopus	45
OpenAlex	38
Web of science	34