Excisive Hierarchical Clustering Methods for Network Data

Abstract

We introduce two practical properties of hierarchical clustering methods for (possibly asymmetric) network data: excisiveness and linear scale preservation. The latter enforces imperviousness to change in units of measure whereas the former ensures local consistency of the clustering outcome. Algorithmically, excisiveness implies that we can reduce… (More)

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@article{Carlsson2016ExcisiveHC, title={Excisive Hierarchical Clustering Methods for Network Data}, author={Gunnar E. Carlsson and Facundo M{\'e}moli and Alejandro Ribeiro and Santiago Segarra}, journal={CoRR}, year={2016}, volume={abs/1607.06339} }