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Model Based Clustering for Mixed Data: clustMD


Damien McPartland, Claire Gormley

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Refereed Original Article
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 vari- ables, leading to a suite of six clustering models that vary in complexity and provide an elegant and uni ed 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
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Advances in Data Analysis and Classification
National University of Ireland, Dublin (UCD)
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