Variable selection for model-based clustering using the integrated complete-data likelihood

DOI10.1007/s11222-016-9670-1zbMath1384.62199arXiv1501.06314OpenAlexW2170744602MaRDI QIDQ133915

Mohammed Sedki, Matthieu Marbac, Mohammed Sedki, Matthieu Marbac

Publication date: 23 May 2016

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.06314

zbMATH Keywords

model-based clusteringvariable selectioninformation criterionGaussian mixture modelparameter inferenceintegrated complete-data likelihood

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)

Related Items

Unifying data units and models in (co-)clustering, Distance Metrics and Clustering Methods for Mixed‐type Data, A survey on model-based co-clustering: high dimension and estimation challenges, Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics, A tractable multi-partitions clustering, Variable selection methods for model-based clustering, VarSelLCM, Variable selection for mixed data clustering: application in human population genomics, Bayesian bi-clustering methods with applications in computational biology, Bayesian inference for infinite asymmetric Gaussian mixture with feature selection

Uses Software

• R
• CRAN
• clustvarsel
• PARVUS
• AS 136
• VarSelLCM
• sparcl
• COSA

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion
• Latent class models for mixed variables with applications in archaeometry
• Variable selection in model-based clustering: a general variable role modeling
• Exact and Monte Carlo calculations of integrated likelihoods for the latent class model
• On the choice of a model to fit data from an exponential family
• Clustering criteria for discrete data and latent class models
• Estimating the dimension of a model
• Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler
• Variable Selection for Clustering with Gaussian Mixture Models
• Clustering Objects on Subsets of Attributes (with Discussion)
• A Framework for Feature Selection in Clustering
• The Bayesian Choice
• Bayesian Variable Selection in Clustering High-Dimensional Data
• Variable Selection for Model-Based Clustering
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