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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 2/2016

01.06.2016 | Regular Article

Model based clustering for mixed data: clustMD

verfasst von: Damien McParland, Isobel Claire Gormley

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2016

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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.
Vorheriger Artikel Micro–macro multilevel latent class models with multiple discrete individual-level variables
Nächster Artikel Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

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Metadaten
Titel
Model based clustering for mixed data: clustMD
verfasst von
Damien McParland
Isobel Claire Gormley
Publikationsdatum
01.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 2/2016
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-016-0238-x

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