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Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro and Santiago Segarra
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
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.
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