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Model Based Clustering for Mixed Data: clustMD
Author(s)
Date Issued
2016-06
Date Available
2017-06-01T01:00:12Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Springer
Journal
Advances in Data Analysis and Classification
Volume
10
Issue
2
Start Page
155
End Page
169
Copyright (Published Version)
2016 Springer
Web versions
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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