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A complex networks approach for data clustering

Author

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  • de Arruda, Guilherme F.
  • Costa, Luciano da Fontoura
  • Rodrigues, Francisco A.

Abstract

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate.

Suggested Citation

  • de Arruda, Guilherme F. & Costa, Luciano da Fontoura & Rodrigues, Francisco A., 2012. "A complex networks approach for data clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6174-6183.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:23:p:6174-6183
    DOI: 10.1016/j.physa.2012.07.007
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    References listed on IDEAS

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    1. J. M. Kumpula & J. Saramäki & K. Kaski & J. Kertész, 2007. "Limited resolution in complex network community detection with Potts model approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(1), pages 41-45, March.
    2. Domany, Eytan, 1999. "Superparamagnetic clustering of data — The definitive solution of an ill-posed problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 263(1), pages 158-169.
    3. Getz, G. & Levine, E. & Domany, E. & Zhang, M.Q., 2000. "Super-paramagnetic clustering of yeast gene expression profiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 279(1), pages 457-464.
    4. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.

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