Abstract
The min and the max hierarchical clustering methods discussed by Johnson are extended to include the use of asymmetric similarity values. The first part of the paper presents the basic min and max procedures but in the context of graph theory; this description is then generalized to directed graphs as a way of introducing the less restrictive characterization of the original clustering techniques.
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Hubert, L. Min and max hierarchical clustering using asymmetric similarity measures. Psychometrika 38, 63–72 (1973). https://doi.org/10.1007/BF02291174
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DOI: https://doi.org/10.1007/BF02291174