M
Michael Fop
Researcher at University College Dublin
Publications - 18
Citations - 2079
Michael Fop is an academic researcher from University College Dublin. The author has contributed to research in topics: Cluster analysis & Feature selection. The author has an hindex of 6, co-authored 14 publications receiving 1343 citations.
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Journal ArticleDOI
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.
TL;DR: This updated version of mclust adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
Journal ArticleDOI
Variable Selection Methods for Model-based Clustering
TL;DR: This review provides a summary of the methods developed for variable selection in model-based clustering and existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.
Journal ArticleDOI
Variable selection methods for model-based clustering
TL;DR: The authors provides a summary of the methods developed for variable selection in model-based clustering, and the existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.
Journal ArticleDOI
Variable Selection for Latent Class Analysis with Application to Low Back Pain Diagnosis
TL;DR: A variable selection method for latent class analysis applied to the selection of the most useful variables in detecting the group structure in the data is proposed and shown to perform a parsimonious variable selection and to give a clustering performance comparable to the expert-based classification of patients into three classes of pain.
Journal ArticleDOI
Model-based clustering with sparse covariance matrices
TL;DR: In this paper, a penalized likelihood approach is employed for estimation and a general penalty term on the graph configurations can be used to induce different levels of sparsity and incorporate prior knowledge.