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Model based clustering of high-dimensional binary data
- Tang, Yang, Browne, Ryan P., McNicholas, Paul D.
- Computational statistics & data analysis 2015 v.87 pp. 84-101
- algorithms, models
- 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 further extended 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. Real and simulated data are used to demonstrate this approach.