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Simple Iterative Heuristics for Correlation Clustering

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Large-Scale Scientific Computing (LSSC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8353))

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Abstract

A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyze the approximation and complexity aspects of this heuristic and its randomized variant where two clusters to merge are chosen uniformly at random among cluster pairs amenable to merge.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Lund University, Lund, Sweden

    Andrzej Lingas

  2. Department of Computer Science, Malmö University, Malmö, Sweden

    Mia Persson

Authors
  1. Andrzej Lingas
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  2. Mia Persson
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Corresponding author

Correspondence to Andrzej Lingas .

Editor information

Editors and Affiliations

  1. Inst. of Info. & Comm. Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

  3. Dept. of Informatics and Mathematical Modell, Technical University of Denmark, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Lingas, A., Persson, M. (2014). Simple Iterative Heuristics for Correlation Clustering. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_29

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  • DOI: https://doi.org/10.1007/978-3-662-43880-0_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43879-4

  • Online ISBN: 978-3-662-43880-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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