Factorial Clustering with an Application to Plant Distribution Data

We propose a latent variable approach for multiple clustering of categorical data. We use logistic regression models for the conditional distribution of observable features given the latent cluster variables. This model supports an interpretation of the different clusterings as representing distinct, independent factors that determine the distribution of the observed features. We apply the model for the analysis of plant distribution data, where multiple clusterings are of interest to determine the major underlying factors that determine the vegetation in a geographical region.