title: Hierarchical Cluster Analysis Using Intraclass Correlations: A Mixture Model Study.
abstract: Sixteen hierarchical clustering algorithms were compared on their ability to resolve 20 multivariate normal mixtures (Blashfield, 1976) and 12 multivariate gamma mixtures (Mojena, 1977). The 16 algorithms represented a 4 x 4 (amalgamation rule x similarity measure) design. The amalgamation rules were single, complete, average, and centroid linkage. The similarity measures were Euclidean distance, correlation, and the one-way and two-way intraclass correlations. Accuracy was calculated using both kappa as a function of coverage (Edelbrock, 1979) and Rand's statistic. Comparisons were also made with Ward's minimum variance technique. Conclusions based on kappa and Rand's statistic were identical. A subset of algorithms was found to be highly accurate in solving both types of mixtures. The accurate algorithms were average and centroid linkage using the one-way intraclass correlation and Ward's technique. This suggests that the one-way intraclass correlation may be useful in clustering applications aimed at identifying subtypes differing in profile shape, elevation, and scatter.
authors.forename: C
authors.initials: C
authors.lastname: Edelbrock
authors.forename: B
authors.initials: B
authors.lastname: McLaughlin