Spatially-Aware Comparison and Consensus for Clusterings

Details Spatially-Aware Comparison and Consensus for Clusterings Spatially-Aware Comparison and Consensus for Clusterings

2011-01-31

arxiv.org/abs/1102.0026v1

Computer Science

Learning

12 Pages, 9 figures, Proceedings of 2011 Siam International Conference on Data Mining

Scientific paper

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.

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