Hierarchical Quasi-Clustering Methods for Asymmetric Networks


Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):352-360, 2014.


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.

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