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

Unlu R., Xanthopoulos P.

ANNALS OF OPERATIONS RESEARCH, vol.276, no.1-2, pp.229-247, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 276 Issue: 1-2
  • Publication Date: 2019
  • Doi Number: 10.1007/s10479-017-2716-8
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.229-247
  • Keywords: Consensus clustering, Unsupervised learning, Weighted consensus clustering, Graph partitioning, CLASSIFICATION, CONSENSUS
  • Abdullah Gül University Affiliated: No


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.