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Efficient similarity-based data clustering by optimal object to cluster reallocation

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  • Mathias Rossignol
  • Mathieu Lagrange
  • Arshia Cont

Abstract

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

Suggested Citation

  • Mathias Rossignol & Mathieu Lagrange & Arshia Cont, 2018. "Efficient similarity-based data clustering by optimal object to cluster reallocation," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-22, June.
  • Handle: RePEc:plo:pone00:0197450
    DOI: 10.1371/journal.pone.0197450
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    Cited by:

    1. Stefan Tönnissen & Jan Heinrich Beinke & Frank Teuteberg, 2020. "Understanding token-based ecosystems – a taxonomy of blockchain-based business models of start-ups," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 307-323, June.
    2. Osbert C Zalay, 2020. "Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-28, January.

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