Binary Dissimilarities and Clustering

Abstract

We revisit partitioning criteria for binary observations and extend their analysis to general dissimilarities, weighted observations, and categorical attributes. Seven aggregation criteria are examined with consistent pair-counting conventions and explicit treatment of singleton classes. A deletion argument establishes monotonicity of the optimum as the number of nonempty classes increases. For the family |C|−α ∑ i