SAGE Journals Online
Advertisement
Sign In to gain access to subscriptions and/or personal tools.

 

Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Advertisement

Sign In to gain access to subscriptions and/or personal tools.
Statistical Methods in Medical Research
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by McLachlan, G J
Right arrow Articles by Chang, S U
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by McLachlan, G J
Right arrow Articles by Chang, S U
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Mixture modelling for cluster analysis

G J McLachlan

Department of Mathematics and the Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia, gjm{at}maths.uq.edu.au

S U Chang

Department of Mathematics, University of Queensland, Brisbane, Australia

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Statistical Methods in Medical Research, Vol. 13, No. 5, 347-361 (2004)
DOI: 10.1191/0962280204sm372ra
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Cancer Epidemiol. Biomarkers Prev.Home page
K. D. Siegmund, A. J. Levine, J. Chang, and P. W. Laird
Modeling exposures for DNA methylation profiles.
Cancer Epidemiol. Biomarkers Prev., March 1, 2006; 15(3): 567 - 572.
[Abstract] [Full Text] [PDF]


Advertisement