Model based clustering of high-dimensional binary data

A mixture of latent trait models with common slope parameters for model-based clustering of high-dimensional binary data, a data type for which few established methods exist, is proposed. Recent work on clustering of binary data, based on a d -dimensional Gaussian latent variable, is extended by incorporating common factor analyzers. Accordingly, this approach facilitates a low-dimensional visual representation of the clusters. The model is …