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Computer Optics, 2017, Volume 41, Issue 5, Pages 712–718 (Mi co441)  

IMAGE PROCESSING, PATTERN RECOGNITION

Centroid averaging algorithm for a clustering ensemble

V. V. Tatarnikova, I. A. Pestunovb, V. B. Berikovc

a Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia
b Institute of Computational Technologies SB RAS, Novosibirsk, Russia
c Novosibirsk State University, Novosibirsk, Russia

Abstract: A collective approach to cluster analysis is considered in the paper. An algorithm of centroid averaging is proposed. The algorithm allows constructing the consensus partition of a dataset into clusters, using a set of partitions built with any centroid-based algorithm. We discuss results of applying the proposed algorithm to modeled data and for the segmentation of hyperspectral images with noise channels. Some details of implementation in a multithreaded environment that allows increasing the algorithm performance are given.

Keywords: clustering ensemble, K-means, centroid, hyperspectral image analysis.

DOI: https://doi.org/10.18287/2412-6179-2017-41-5-712-718

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Full text: http://www.computeroptics.smr.ru/.../410514.html
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Received: 26.04.2017
Accepted:13.09.2017

Citation: V. V. Tatarnikov, I. A. Pestunov, V. B. Berikov, “Centroid averaging algorithm for a clustering ensemble”, Computer Optics, 41:5 (2017), 712–718

Citation in format AMSBIB
\Bibitem{TatPesBer17}
\by V.~V.~Tatarnikov, I.~A.~Pestunov, V.~B.~Berikov
\paper Centroid averaging algorithm for a clustering ensemble
\jour Computer Optics
\yr 2017
\vol 41
\issue 5
\pages 712--718
\mathnet{http://mi.mathnet.ru/co441}
\crossref{https://doi.org/10.18287/2412-6179-2017-41-5-712-718}


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