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This article is cited in 6 scientific papers (total in 6 papers)
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.
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
Linking options:
https://www.mathnet.ru/eng/co441 https://www.mathnet.ru/eng/co/v41/i5/p712
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