Model-based linear clustering

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  1. Department of Statistics
  2. Model-based linear clustering

Model-based linear clustering

TitleModel-based linear clustering
Publication TypeJournal Article
Year of Publication2010
AuthorsYan, G, Welch, WJ, Zamar, RH
JournalCANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
Volume38
Pagination716-737
Date PublishedDEC
Type of ArticleArticle
ISSN0319-5724
KeywordsEM algorithm, errors in-variables model, linear cluster, mixture model, orthogonal regression, profile likelihood
AbstractThe authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set For each linear cluster an errors in variables model is assumed The optimization of the derived profile likelihood can be achieved by an EM algorithm Its asymptotic properties and its relationships with several existing clustering methods are discussed Methods to determine the number of components in a data set are adapted to this linear clustering setting Several simulated and real data sets are analyzed for comparison and illustration purposes The Canadian Journal of Statistics 38 716-737 2010 (C) 2010 Statistical Society of Canada
DOI10.1002/cjs.10082