Bayesian LDA for mixed-membership clustering analysis: The Rlda package

摘要

The goal of this paper is to present the Rlda package for mixed-membership clustering analysis based on the Latent Dirichlet Allocation model adapted to different types of data (i.e., Multinomial, Bernoulli, and Binomial entries). We present the corresponding statistical models and illustrate their use with several examples using the Rlda package. Because these types of data frequently emerge in fields as disparate as ecology, remote sensing, marketing, and finance, we believe this package will be of broad interest for users interested in unsupervised pattern recognition, particularly mixed-membership clustering analysis for categorical data.