Spatially-Aware Comparison and Consensus for Clusterings

This paper proposes a new distance metric between clusterings that incorporates information about the spatial distribution of points and clusters. Our approach builds on the idea of a Hilbert space-based representation of clusters as a combination of the representations of their constituent points. We use this representation and the underlying metric to design a spatially-aware consensus clustering procedure. This consensus procedure is implemented via a novel reduction to Euclidean clustering, and is both simple and efficient. All of our results apply to both soft and hard clusterings. We accompany these algorithms with a detailed experimental evaluation that demonstrates the efficiency and quality of our techniques.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Parasaran Raman, Jeff M. Phillips, Suresh Venkatasubramanian
• Keywords: Constituent Points, CORR 2011, Education, Euclidean Clustering, Hilbert Space-based Representation