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.