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Model Based Clustering for Mixed Data: clustMD

Authors: 

Damien McPartland, Claire Gormley

Publication Type: 
Refereed Original Article
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 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
Digital Object Identifer (DOI): 
10.1007/s11634-016-0238-x
Publication Status: 
Published
Publication Date: 
12/02/2016
Journal: 
Advances in Data Analysis and Classification
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
Yes
Publication document: