Ensemble clustering by means of clustering embedding in vector spaces

• This paper presents a novel approach to solving the ensemble clustering problem.
• An embedding method successful for strings and graphs is adapted to clusterings.
• An extensive experiment study is presented.
• The proposed method is superior to several state-of-the-art related algorithms.

Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance.