Metric admissibility and agglomerative clustering
作者:
Zhenmin Chen,
John W. Van Ness,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1994)
卷期:
Volume 23,
issue 3
页码: 833-845
ISSN:0361-0918
年代: 1994
DOI:10.1080/03610919408813202
出版商: Marcel Dekker, Inc.
关键词: metric admissible clustering;agglomerative clustering;Lance and Williams algorithm;admissible clustering
数据来源: Taylor
摘要:
A new clustering admissibility condition, metric admissibility,is introduced. This admissibility condition is important in clustering applications where it is desired that the cluster distances remain metric(satisfy the triangle inequality).The Lance and Williams infinite family of clusteringalgorithms is evaluated with respect to this admissibility condition. This family contains most ofthe commonly used agglomerative clustering algorithms. Necessary and sufficient conditions are given on the parameters of the Lance and Williams cluster distance function in order to assure metricadmissibility of the corresponding algorithms
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