On comparative study of clustering using finite mixture of non-Gaussian distributions

DOI10.1007/978-981-19-0471-4_12OpenAlexW4226027456MaRDI QIDQ2091587

R. Deepana, C. Kiruthika

Publication date: 1 November 2022

Full work available at URL: https://doi.org/10.1007/978-981-19-0471-4_12

zbMATH Keywords

EM algorithmmodel selectionmodel-based clusteringskewed distributionsfinite mixture models

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Robust mixture modeling based on scale mixtures of skew-normal distributions
• Stable and visualizable Gaussian parsimonious clustering models
• Choosing initial values for the EM algorithm for finite mixtures
• Finite mixture models and model-based clustering
• Parameter estimation for finite mixture distributions
• Geometric skew normal distribution
• Finite mixture and Markov switching models.
• Model-based clustering
• Mixtures
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Skew Laplace Finite Mixture Modelling
• Extending mixtures of multivariate \(t\)-factor analyzers