APPROXIMATIONS TO BAYESIAN CLUSTERING RULES
成果类型：
Article
署名作者：
BINDER, DA
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/68.1.275
发表日期：
1981
页码：
275285
关键词：
摘要：
The amount of computation required for implementing the Bayesian cluster analysis suggested by Binder (1978) is often too large for exact results to be feasible. A general algorithm is proposed for approximating the similarity matrix and the resulting optimal partition. This algorithm is applied to artificial and to real data. For the real data, it appears that the algorithm is successful at identifying the optimal partitions as well as those units whose group membership is doubtful. [Data included Fisher''s (1936) Iris morphological measurements and Duncan''s (1955) barley variety yields.].