HIERARCHICAL CLUSTERING SCHEMES
HIERARCHICAL CLUSTERING SCHEMES
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DOI：
10.1007/bf02289588
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发表时间：
1967-01-01
期刊：
影响因子：
3
通讯作者：
JOHNSON, SC
中科院分区：
文献类型：
--
作者：
JOHNSON, SC
在这些对象之间的相似性的经验措施的基础上，将对象划分为最优同质组的技术在几个不同的领域受到越来越多的关注。本文发展了这种簇的任何层次系统与特定类型的距离度量之间的有用对应关系。这种对应关系产生了两种聚类方法，它们在数据单调变换下计算速度快且不变性。在明确定义的意义上，一种方法形成最佳“连接”的集群，而另一种方法形成最佳“紧凑”的集群。
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.”