Model Based Clustering for Mixed Data: clustMD
|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 learning; Statistics; Latent variables; Mixture models; Mixed data; Monte 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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