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A weighted framework for unsupervised ensemble learning based on internal quality measures

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  • Ramazan Ünlü

    (University of Central Florida)

  • Petros Xanthopoulos

    (Stetson University)

Abstract

Unsupervised ensemble, or consensus clustering, consists in finding the optimal combination strategy of individual clusterings that is robust with respect to the selection of an algorithmic clustering pool. Recently an approach was proposed based on the concept of consensus graph that has profound advantages over its predecessors. Despite its robust properties this approach assigns the same weight to the contribution of each clustering to the final solution. In this paper, we propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other popular approaches. Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties.

Suggested Citation

  • Ramazan Ünlü & Petros Xanthopoulos, 2019. "A weighted framework for unsupervised ensemble learning based on internal quality measures," Annals of Operations Research, Springer, vol. 276(1), pages 229-247, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-017-2716-8
    DOI: 10.1007/s10479-017-2716-8
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    References listed on IDEAS

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    Cited by:

    1. Kanchan Jha & Sriparna Saha & Pratik Dutta, 2024. "Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach," Annals of Operations Research, Springer, vol. 339(3), pages 1793-1811, August.
    2. Triantaphyllou, Evangelos & Yanase, Juri & Hou, Fujun, 2020. "Post-consensus analysis of group decision making processes by means of a graph theoretic and an association rules mining approach," Omega, Elsevier, vol. 94(C).

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