Model Based Clustering of High-Dimensional Binary Data
Abstract
We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a -dimensional Gaussian latent variable, is extended by incorporating common factor analyzers. Accordingly, our approach facilitates a low-dimensional visual representation of the clusters. We extend the model further by the incorporation of random block effects. The dependencies in each block are taken into account through block-specific parameters that are considered to be random variables. A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters. Our approach is demonstrated on real and simulated data.
Cite
@article{arxiv.1404.3174,
title = {Model Based Clustering of High-Dimensional Binary Data},
author = {Yang Tang and Ryan P. Browne and Paul D. McNicholas},
journal= {arXiv preprint arXiv:1404.3174},
year = {2017}
}