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based clustering and classification. These make use of backward or forward procedures to define the roles of the variables. Unfortunately, such stepwise procedures are slow and the resulting algorithms inefficient when analyzing large data sets with many variables. In this paper, we propose an alternative regularization approach for variable selection in model-based clustering and classification. In our approach the variables are first ranked using a lassolike procedure in order to avoid slowdoi:10.1007/s11634-018-0322-5 fatcat:wjttmzvjkvb23ecqx6s2a6sake