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Metaheuristically Optimized Multicriteria Clustering for Medium-Scale Networks

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Soft Computing Models in Industrial and Environmental Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 188))

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Abstract

We present a highly scalable metaheuristic approach to complex network clustering. Our method uses a multicriteria construction procedure (MCP), controlled by adaptable constraints of local density and local connectivity. The input of the MCP - the permutation of vertices, is evolved using a metaheuristic based on local search. Our approach provides a favorable computational complexity of the MCP for sparse graphs and an adaptability of the constraints, since the criteria of a ”good clustering” are still not generally agreed upon in the literature. Experimental verification, regarding the quality and running time, is performed on several well-known network clustering instances, as well as on real-world social network data.

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Author information

Authors and Affiliations

  1. Institute of Applied Informatics, Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovičova 3, 842 16, Bratislava, Slovakia

    David Chalupa & Jiří Pospíchal

Authors
  1. David Chalupa
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  2. Jiří Pospíchal
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Corresponding author

Correspondence to David Chalupa .

Editor information

Editors and Affiliations

  1. VŠB-TU Ostrava, 17. listopadu 15, Ostrava, 70833, Czech Republic

    Václav Snášel

  2. (MIR Labs), Scientific Network for Innovation and, Machine Intelligence Research Labs, Auburn, 98071-2259, Washington, USA

    Ajith Abraham

  3. , Dept. de Informática y Automática, University of Salamanca, Plaza de la Merced, s/n, Salamanca, 37008, Spain

    Emilio S. Corchado

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Chalupa, D., Pospíchal, J. (2013). Metaheuristically Optimized Multicriteria Clustering for Medium-Scale Networks. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_35

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  • DOI: https://doi.org/10.1007/978-3-642-32922-7_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32921-0

  • Online ISBN: 978-3-642-32922-7

  • eBook Packages: EngineeringEngineering (R0)

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