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与相似性度量相关的弱层次结构——一种加法聚类技术。

Weak hierarchies associated with similarity measures--an additive clustering technique.

作者信息

Bandelt H J, Dress A W

出版信息

Bull Math Biol. 1989;51(1):133-66. doi: 10.1007/BF02458841.

Abstract

A new and apparently rather useful and natural concept in cluster analysis is studied: given a similarity measure on a set of objects, a sub-set is regarded as a cluster if any two objects a, b inside this sub-set have greater similarity than any third object outside has to at least one of a, b. These clusters then form a closure system which can be described as a hypergraph without triangles. Conversely, given such a system, one may attach some weight to each cluster and then compose a similarity measure additively, by letting the similarity of a pair be the sum of weights of the clusters containing that particular pair. The original clusters can be reconstructed from the obtained similarity measure. This clustering model is thus located between the general additive clustering model of Shepard and Arabie (1979) and the standard hierarchical model. Potential applications include fitting dendrograms with few additional nonnested clusters and simultaneous representation of some families of multiple dendrograms (in particular, two-dendrogram solutions), as well as assisting the search for phylogenetic relationships by proposing a somewhat larger system of possibly relevant "family groups", from which an appropriate choice (based on additional insight or individual preferences) remains to be made.

摘要

研究了聚类分析中一个新的、显然相当有用且自然的概念:给定一组对象上的相似性度量,如果该子集中的任意两个对象a、b之间的相似性大于该子集外的任何第三个对象与a、b中至少一个对象的相似性,则该子集被视为一个聚类。这些聚类进而形成一个封闭系统,可描述为一个无三角形的超图。反之,给定这样一个系统,可以给每个聚类赋予一些权重,然后通过让一对对象的相似性为包含该特定对的聚类的权重之和,以加法方式构成一个相似性度量。原始聚类可以从得到的相似性度量中重建。因此,这个聚类模型介于Shepard和Arabie(1979)的一般加法聚类模型和标准层次模型之间。潜在应用包括拟合带有少量额外非嵌套聚类的树状图以及同时表示多个树状图的某些族(特别是双树状图解),以及通过提出一个可能相关的“家族组”的稍大系统来协助寻找系统发育关系,从中仍需(基于额外的见解或个人偏好)做出合适的选择。

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