Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Abstract
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect to two desirable properties. We prove that a modified version of single linkage is the only admissible quasi-clustering method. Moreover, we show stability of the proposed method and we establish invariance properties fulfilled by it. Algorithms are further developed and the value of quasi-clustering analysis is illustrated with a study of internal migration within United States.
Cite
Text
Carlsson et al. "Hierarchical Quasi-Clustering Methods for Asymmetric Networks." International Conference on Machine Learning, 2014.Markdown
[Carlsson et al. "Hierarchical Quasi-Clustering Methods for Asymmetric Networks." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/carlsson2014icml-hierarchical/)BibTeX
@inproceedings{carlsson2014icml-hierarchical,
title = {{Hierarchical Quasi-Clustering Methods for Asymmetric Networks}},
author = {Carlsson, Gunnar and Mémoli, Facundo and Ribeiro, Alejandro and Segarra, Santiago},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {352-360},
volume = {32},
url = {https://mlanthology.org/icml/2014/carlsson2014icml-hierarchical/}
}