Euclidean Distances, soft and spectral Clustering on Weighted Graphs

Details Euclidean Distances, soft and spectral Clustering on Weighted Graphs Euclidean Distances, soft and spectral Clustering on Weighted Graphs

2010-07-06

arxiv.org/abs/1007.0832v1

ECML PKDD 2010, J.L. Balc\'azar et al. (Eds.): ECML PKDD 2010, Part I, LNAI 6321, pp. 103-118

Statistics

Machine Learning

accepted for presentation (and further publication) at the ECML PKDD 2010 conference

Scientific paper

10.1007/978-3-642-15880-3

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow data, properly conditioned, illustrate the procedure as well as visualization aspects.

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