Euclidean Distances, Soft and Spectral Clustering on Weighted Graphs
Abstract
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.
Cite
Text
Bavaud. "Euclidean Distances, Soft and Spectral Clustering on Weighted Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15880-3_13Markdown
[Bavaud. "Euclidean Distances, Soft and Spectral Clustering on Weighted Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/bavaud2010ecmlpkdd-euclidean/) doi:10.1007/978-3-642-15880-3_13BibTeX
@inproceedings{bavaud2010ecmlpkdd-euclidean,
title = {{Euclidean Distances, Soft and Spectral Clustering on Weighted Graphs}},
author = {Bavaud, François},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {103-118},
doi = {10.1007/978-3-642-15880-3_13},
url = {https://mlanthology.org/ecmlpkdd/2010/bavaud2010ecmlpkdd-euclidean/}
}