Gaussian-based visualization of Gaussian and non-Gaussian-based clustering

DOI10.1007/s00357-020-09369-yOpenAlexW3034514468MaRDI QIDQ2040030

Vincent Vandewalle, Christophe Biernacki, Matthieu Marbac

Publication date: 9 July 2021

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-020-09369-y

zbMATH Keywords

visualizationGaussian mixturedimension reductionmodel-based clusteringlinear discriminant analysisfactorial analysis

Mathematics Subject Classification ID

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

Related Items

ClusVis

Uses Software

• mclust
• fda (R)
• FactoMineR
• PCAmixdata
• funFEM
• funHDDC
• Rmixmod
• clustMD
• CoModes
• GTM

Cites Work

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• Model-based clustering of time series in group-specific functional subspaces
• Model-based clustering and segmentation of time series with changes in regime
• The discriminative functional mixture model for a comparative analysis of bike sharing systems
• Handling missing values with regularized iterative multiple correspondence analysis
• Clustering bivariate mixed-type data via the cluster-weighted model
• Dimension reduction for model-based clustering via mixtures of multivariate \(t\)-distributions
• Methods for merging Gaussian mixture components
• Orthogonal rotation in PCAMIX
• Mixture of latent trait analyzers for model-based clustering of categorical data
• Fast online graph clustering via Erdős-Rényi mixture
• Latent class models for mixed variables with applications in archaeometry
• Another interpretation of the EM algorithm for mixture distributions
• Self-organized formation of topologically correct feature maps
• Clustering criteria for discrete data and latent class models
• Estimating the dimension of a model
• Model-based clustering for multivariate functional data
• A semiparametric and location-shift copula-based mixture model
• Multiple imputation in principal component analysis
• A principal component method to impute missing values for mixed data
• Model based clustering for mixed data: clustMD
• Latent class model with conditional dependency per modes to cluster categorical data
• Functional data analysis.
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-based clustering of Gaussian copulas for mixed data
• New Consistent and Asymptotically Normal Parameter Estimates for Random-Graph Mixture Models
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Multiple imputation for continuous variables using a Bayesian principal component analysis
• Mixture Model-Based Classification
• Localized Functional Principal Component Analysis
• A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
• Regularised PCA to denoise and visualise data