40. The Adaptive Mean-Linkage Algorithm: A Botton-Up Hierarchical Cluster Technique

In this paper a variant of the classical hierarchical cluster analysis is reported.
This agglomerative cluster technique is referred to as the Adaptive Mean-Linkage Algorithm.
It can be interpreted as a linkage algorithm where the value of the threshold is conveniently
up-dated at each interaction. The superiority of the adaptive clustering with respect to the
average-linkage algorithm follows because it achieves a good compromise on threshold values:
Thresholds based on the cut-off distance are sufficiently small to assure the homogeneity
and also large enough to guarantee at least a pair of merging sets.
This approach is applied to a set of possible substituents in a chemical series.