@article { ISI:000284674900012,
	title = {Model-based linear clustering},
	journal = {CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE},
	volume = {38},
	number = {4},
	year = {2010},
	month = {DEC},
	pages = {716-737},
	publisher = {WILEY-BLACKWELL},
	type = {Article},
	address = {COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA},
	abstract = {The 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},
	keywords = {EM algorithm, errors in-variables model, linear cluster, mixture model, orthogonal regression, profile likelihood},
	issn = {0319-5724},
	doi = {10.1002/cjs.10082},
	author = {Yan, Guohua and Welch, William J. and Zamar, Ruben H.}
}
