Centroid averaging algorithm for a clustering ensemble

Vadim Vladimirovich Tatarnikov, Igor Alekseevich Pestunov, Vladimir Borisovich Berikov

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)712-718
Number of pages7
JournalComputer Optics
Issue number5
Publication statusPublished - 1 Sep 2017


  • Centroid
  • Clustering ensemble
  • Hyperspectral image analysis
  • K-means


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