Latent class models for clustering : a comparison with K-means

Recent developments in latent class (LC) analysis and associated software to include continuous variables offer a model-based alternative to more traditional clustering approaches such as K-means. In this paper, the authors compare these two approaches using data simulated from a setting where true group membership is known. The authors choose a setting favourable to K-means by simulating data according to the assumptions made in both discriminant analysis (DISC) and K-means clustering. Since the information on true group membership is used in DISC but not in clustering approaches in general, the authors use the results obtained from DISC as a gold standard in determining an upper bound on the best possible…