自适应均值链接算法：一种自底向上的层次聚类技术
The Adaptive Mean-Linkage Algorithm: A Bottom-Up Hierarchical Cluster Technique
摘要：
本文报道了经典的层次聚类分析的一种变体。这种凝聚（自下而上）聚类技术被称为自适应均值链接算法。它可以被解释为一种链接算法，其中阈值的值在每次交互时方便地更新。自适应聚类相对于平均链接算法的优越性如下，因为它在阈值上实现了良好的折衷：基于截止距离的阈值足够小以确保同质性，并且也足够大以保证至少一对合并集。这种方法适用于化学系列中一组可能的取代基。
英文摘要：
In this paper a variant of the classical hierarchical cluster analysis is reported. This agglomerative (bottom-up) 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.
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