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

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 a...

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Bibliographic Details
Published inAnnals of operations research Vol. 276; no. 1-2; pp. 229 - 247
Main Authors Ünlü, Ramazan, Xanthopoulos, Petros
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2019
Springer
Springer Nature B.V
Subjects
Algorithms
Analysis
Business and Management
Cluster analysis
Clustering
Combinatorial optimization
Combinatorics
Ensemble learning
Machine learning
Operations research
Operations Research/Decision Theory
Robustness
S.I.: Computational Biomedicine
Theory of Computation
Graph partitioning
Weighted consensus clustering
Consensus clustering
Unsupervised learning
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Summary: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.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-017-2716-8