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

作者：Jay Magidson, Jeroen K. Vermunt · 发表于：Data Archiving and Networked Services (DANS) · 年份：2002 · 被引用次数：760 · 研究领域：Bayesian Methods and Mixture Models、Advanced Clustering Algorithms Research、Face and Expression Recognition

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 outcome that might be expected from a clustering technique. The results indicate that LC substantially outperforms the K-means technique. A truly surprising result is that the LC performance is so good that it is virtually indistinguishable from the performance of DISC.