Latent Class Cluster Models
Traditional clustering approaches utilize unsupervised classification algorithms that group cases together that are "near" each other according to an ad hoc definition of "distance". In the last decade interest has shifted towards model based approaches, especially mixture model clustering where each latent class represents a hidden cluster. Today's high-speed computers make these computationally intensive methods for model based approaches practical.
Latent GOLD's cluster module provides the state-of-the-art in cluster analysis based on latent class models. It is appropriate to include not only continuous variables, but also variables that are ordinal, nominal or counts, or any combination of these.
Latent GOLD improves over traditional approaches, using model based probabilities to classify cases into the appropriate cluster.
Covariates also can be included directly in the model for improved cluster description. In addition, the latent class cluster model can be used as a traditional latent class model, handling measurement and classification errors in categorical variables.