Hierarchical Cluster Analysis Using Intraclass Correlations: A Mixture Model Study

Multivariate Behavioral Research
C Edelbrock, B McLaughlin

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.

Citations

Nov 1, 1994·Journal of Clinical Psychology·G D ZimetT Zimmerman
Jul 1, 1991·Multivariate Behavioral Research·K Schweizer
Aug 13, 1999·Breast Cancer Research and Treatment·T F Hack, L F Degner
Jul 1, 1983·Multivariate Behavioral Research·L C MoreyH A Skinner
Sep 19, 2021·Critical Care : the Official Journal of the Critical Care Forum·Faheem W GuirgisSrinivasa T Reddy

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