Adaptive Hierarchical Clustering Schemes

Adaptive hierarchical clustering schemes. Syst. Zool., 18:58–82.—Various methods of summarizing phenetic relationships are briefly reviewed (including a comparison of principal components analysis and non-metric scaling). Sequential agglomerative hierarchical clustering schemes are considered in particular detail, and several new methods are proposed. The new algorithms are characterized by their ability to “adapt” to the possible trends of variation found within clusters as they are being formed. A nonlinear version allows the isolation and description of clusters which are parabolic, ring-shaped, etc., by the introduction of appropriate dummy variables. Procedures for computing the best fitting trend line through the cluster are also presented, and problems in measuring the amount of information lost by clustering are discussed.