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Variable selection in model-based clustering: A general variable role modeling
2009
Computational Statistics & Data Analysis
The currently available variable selection procedures in model-based clustering assume that the irrelevant clustering variables are all independent or are all linked with the relevant clustering variables. We propose a more versatile variable selection model which describes three possible roles for each variable: The relevant clustering variables, the irrelevant clustering variables dependent on a part of the relevant clustering variables and the irrelevant clustering variables totally
doi:10.1016/j.csda.2009.04.013
fatcat:nvxurn4mqrberdbpmvtd3mqshm