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Graph Clustering Based Model Building

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6238))

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Abstract

Probabilistic models of high-order statistics, capable of expressing complex variable interactions, have been successfully applied by estimation of distribution algorithms (EDAs) to render hard problems tractable. Unfortunately, the dependence structure induction stage in these methods imposes a high computational cost that often dominates the overall complexity of the whole search process.

In this paper, a new unsupervised model induction strategy built upon a maximum flow graph clustering technique is presented. The new approach offers a model evaluation free, fast, scalable, easily parallelizable method, capable of complex dependence structure induction. The method can be used to infer different classes of probabilistic models.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Sapientia Hungarian University of Transylvania, Şoseaua Sighişoarei 1C, 547367, Corunca, Romania

    David Iclănzan

  2. Department of Computer Science, Babe s-Bolyai University, Kogălniceanu no. 1, 400084, Cluj-Napoca, Romania

    David Iclănzan & Dumitru Dumitrescu

Authors
  1. David Iclănzan
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  2. Dumitru Dumitrescu
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of ComputerScience, AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Robert Schaefer

  2. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  3. Department of Mathematics and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309, Bielsko-Biala, Poland

    Joanna Kołodziej

  4. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

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Iclănzan, D., Dumitrescu, D. (2010). Graph Clustering Based Model Building. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_51

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  • DOI: https://doi.org/10.1007/978-3-642-15844-5_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15843-8

  • Online ISBN: 978-3-642-15844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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