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arXiv · 1511.01720

Model Based Clustering for Mixed Data: clustMD

Damien McParland·Isobel Claire Gormley
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Abstract

A model based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussian distributions, generates the observed data of mixed type. The observed data may be any combination of continuous, binary, ordinal or nominal variables. clustMD employs a parsimonious covariance structure for the latent variables, leading to a suite of six clustering models that vary in complexity and provide an elegant and unified approach to clustering mixed data. An expectation maximisation (EM) algorithm is used to estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is required. The clustMD model is illustrated by clustering simulated mixed type data and prostate cancer patients, on whom mixed data have been recorded.

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Damien McParland, Isobel Claire Gormley. 2015-11-05. Model Based Clustering for Mixed Data: clustMD. https://arxiv.org/abs/1511.01720

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