Model based clustering for mixed data: clustMD

Damien McParland () and Isobel Claire Gormley ()
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Damien McParland: University College Dublin
Isobel Claire Gormley: University College Dublin

Advances in Data Analysis and Classification, 2016, vol. 10, issue 2, No 3, 155-169

Abstract: 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.

Keywords: Latent variables; Mixture model; Mixed data; Monte Carlo EM; 62; 6207; 62FXX; 62HXX; 62H30; 68T10; 91C20; 62P10 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11634-016-0238-x

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