Abstract:
:In this paper we review methods of cluster analysis in the context of classifying patients on the basis of clinical and/or laboratory type observations. Both hierarchical and non-hierarchical methods of clustering are considered, although the emphasis is on the latter type, with particular attention devoted to the mixture likelihood-based approach. For the purposes of dividing a given data set into g clusters, this approach fits a mixture model of g components, using the method of maximum likelihood. It thus provides a sound statistical basis for clustering. The important but difficult question of how many clusters are there in the data can be addressed within the framework of standard statistical theory, although theoretical and computational difficulties still remain. Two case studies, involving the cluster analysis of some haemophilia and diabetes data respectively, are reported to demonstrate the mixture likelihood-based approach to clustering.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
McLachlan GJdoi
10.1177/096228029200100103subject
Has Abstractpub_date
1992-01-01 00:00:00pages
27-48issue
1eissn
0962-2802issn
1477-0334journal_volume
1pub_type
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