Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion^[*]

Abstract

A basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.

Key-Words

Akaike Information Criterion - Kullback-Leibler Information - Multinomial

^* This research is supported in part by a grant from the National Cancer Institute and an award from the Alexander von Humboldt Foundation.

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