English
资源说明：Techniques for partitioning objects into optimally homogeneous
groups on the basis of empirical measures of similarity among those objects
have received increasing attention in several different fields. This paper
develops a useful correspondence between any hierarchical system of such
clusters, and a particular type of distance measure. The correspondence
gives rise to two methods of clustering that are computationally rapid and
invariant under monotonic transformations of the data. In an explicitly
defined sense, one method forms clusters that are optimally "connected,"
while the other forms clusters that are optimally "compact."
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