How does latent class cluster analysis compare with the traditional clustering procedures in SAS and SPSS?
A. LC clustering is model-based in contrast to traditional approaches that are based on ad-hoc distance measures. The general probability model underlying LC clustering more readily accommodates reality by allowing for unequal variances in each cluster, use of variables with mixed scale types, and formal statistical procedures for determining the number of clusters, among many other improvements. For a detailed comparison showing how LC cluster outperforms SPSS K-means clustering and SAS FASTCLUS procedures, see Latent Class Modeling as a Probabilistic Extension of K-means Clustering. Q. How does Latent GOLD® classify cases into latent classes. A. Cases are assigned to the latent class having the highest posterior membership probability. Covariates can be added to the model for improved description and prediction of the latent classes.
