Model Based Clustering for Mixed Data: clustMD

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Title: Model Based Clustering for Mixed Data: clustMD
Authors: McParland, Damien
Gormley, Isobel Claire
Permanent link: http://hdl.handle.net/10197/8335
Date: Jun-2016
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Copyright (published version): 2016 Springer
Keywords: Machine learningStatisticsLatent variablesMixture modelsMixed dataMonte Carlo EM
DOI: 10.1007/s11634-016-0238-x
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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