IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i23p6174-6183.html
   My bibliography  Save this article

A complex networks approach for data clustering

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112006693
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2012.07.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    2. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    3. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    4. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.
    5. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    6. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    7. Dugué, Nicolas & Perez, Anthony, 2022. "Direction matters in complex networks: A theoretical and applied study for greedy modularity optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    8. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    9. Wang, Yuyao & Bu, Zhan & Yang, Huan & Li, Hui-Jia & Cao, Jie, 2021. "An effective and scalable overlapping community detection approach: Integrating social identity model and game theory," Applied Mathematics and Computation, Elsevier, vol. 390(C).
    10. Fu, Xianghua & Liu, Liandong & Wang, Chao, 2013. "Detection of community overlap according to belief propagation and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 941-952.
    11. Dafne E. van Kuppevelt & Rena Bakhshi & Eelke M. Heemskerk & Frank W. Takes, 2022. "Community membership consistency applied to corporate board interlock networks," Journal of Computational Social Science, Springer, vol. 5(1), pages 841-860, May.
    12. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    13. Kyle F Davis & Paolo D'Odorico & Francesco Laio & Luca Ridolfi, 2013. "Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    14. Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    15. Fu, Jingcheng & Wu, Jianliang & Liu, Chuanjian & Xu, Jin, 2016. "Leaders in communities of real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 428-441.
    16. Lan Huang & Guishen Wang & Yan Wang & Enrico Blanzieri & Chao Su, 2013. "Link Clustering with Extended Link Similarity and EQ Evaluation Division," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-18, June.
    17. Raghvendra Mall & Rocco Langone & Johan A K Suykens, 2014. "Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    18. Zhang, Hongli & Gao, Yang & Zhang, Yue, 2018. "Overlapping communities from dense disjoint and high total degree clusters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 286-298.
    19. Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    20. Stefano Bianchini & Moritz Muller & Pierre Pelletier & Kevin Wirtz, 2021. "Global health science leverages established collaboration network to fight COVID-19," Papers 2102.00298, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:23:p:6174-6183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.