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Incremental Spectral Clustering

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Learning in Non-Stationary Environments

Abstract

In the present contribution, a novel algorithm for off-line spectral clustering algorithm is introduced and an online extension is derived in order to deal with sequential data. The proposed algorithm aims at dealing with nonconvex clusters having different forms. It relies on the notion of communicability that allows to handle the contiguity of data distribution. In the second part of the paper, an incremental extension of the fuzzy c-varieties is proposed to serve as a building block of the incremental spectral clustering algorithm (ISC). Initial simulations are presented towards the end of the contribution to show the performance of the ISC algorithm.

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Author information

Authors and Affiliations

  1. Institute of Informatics-Systems, University of Klagenfurt, Klagenfurt, Austria

    Abdelhamid Bouchachia

  2. Carinthia University of Applied Sciences, Spittal an der Drau, Austria

    Markus Prossegger

Authors
  1. Abdelhamid Bouchachia
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  2. Markus Prossegger
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Correspondence to Abdelhamid Bouchachia .

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Editors and Affiliations

  1. , Départment Informatique et Automatique, Ecole des Mines de Douai, 941, Rue Charles Bourseul, Douai cedex, 59508, France

    Moamar Sayed-Mouchaweh

  2. University of Linz, Weissdornweg 16, Linz, 4232, Austria

    Edwin Lughofer

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Bouchachia, A., Prossegger, M. (2012). Incremental Spectral Clustering. In: Sayed-Mouchaweh, M., Lughofer, E. (eds) Learning in Non-Stationary Environments. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8020-5_4

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  • DOI: https://doi.org/10.1007/978-1-4419-8020-5_4

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  • Print ISBN: 978-1-4419-8019-9

  • Online ISBN: 978-1-4419-8020-5

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