Regarding the Complexity of Additive Clustering Models: Comment on Lee (2001)

The additive clustering approach to modeling pairwise similarity of entities is a powerful tool for deriving featural stimulus representations. In a recent paper, Lee (2001) proposes a statistically principled measure for choosing between clustering models that accounts for model complexity as well as data Þt. Importantly, complexity is understood to be a property, not merely of the number of clusters, but also their size and pattern of overlap. However, some caution is required when interpreting the measure, with regard to the applicability of the Hadamard inequality to the complexity matrix. ∗Correspondence concerning this article should be addressed to: Daniel Navarro, Department of Psychology, Ohio State University, 1827 Neil Avenue Mall, Columbus OH 43210. Telephone: (614) 688-4071, Facsimile: (614) 292-5601, E-mail: [email protected]