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MCC: a Multiple Consensus Clustering Framework

Author

Listed:
  • Tao Li

    (Florida International University)

  • Yi Zhang
  • Dingding Wang

    (Florida Atlantic University)

  • Jian Xu

Abstract

Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could differ significantly. In this paper, we develop a new framework, called Multiple Consensus Clustering (MCC), to explore multiple clustering views of a given dataset from a set of input clusterings. Instead of generating a single consensus, we propose two sets of approaches to obtain multiple consensus. One employs the meta clustering method, and the other uses a hierarchical tree structure and further applies a dynamic programming algorithm to generate a flat partition from the hierarchical tree using the modularity measure. Multiple consensuses are finally obtained by applying consensus clustering algorithms to each cluster of the partition. Extensive experimental results on 11 real-world datasets and a case study on a Protein-Protein Interaction (PPI) dataset demonstrate the effectiveness of the MCC framework.

Suggested Citation

  • Tao Li & Yi Zhang & Dingding Wang & Jian Xu, 2019. "MCC: a Multiple Consensus Clustering Framework," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 414-434, October.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:3:d:10.1007_s00357-019-09318-4
    DOI: 10.1007/s00357-019-09318-4
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    References listed on IDEAS

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    1. Alexander Strehl & Joydeep Ghosh, 2003. "Relationship-Based Clustering and Visualization for High-Dimensional Data Mining," INFORMS Journal on Computing, INFORMS, vol. 15(2), pages 208-230, May.
    2. Fallah Shafagh & Tritchler David & Beyene Joseph, 2008. "Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-25, August.
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