Identifying connected components in Gaussian finite mixture models for clustering

DOI10.1016/J.CSDA.2015.01.006zbMath1468.62174OpenAlexW2055747151MaRDI QIDQ1660183

Luca Scrucca

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.01.006

zbMATH Keywords

connected componentscluster analysishigh density regionscluster coresfinite mixture of Gaussian distributions

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (7)

Editorial: The third special issue on advances in mixture models ⋮ Modal clustering asymptotics with applications to bandwidth selection ⋮ The Modal Age of Statistics ⋮ Mixture model modal clustering ⋮ Growth mixture modeling with measurement selection ⋮ Manly transformation in finite mixture modeling ⋮ Better than the best? Answers via model ensemble in density-based clustering

Uses Software

• mclust
• R
• Qhull
• geometry
• pdfCluster

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On mixtures of skew normal and skew \(t\)-distributions
• Methods for merging Gaussian mixture components
• An advancement in clustering via nonparametric density estimation
• An entropy criterion for assessing the number of clusters in a mixture model
• Learning from incomplete data via parameterized \(t\) mixture models through eigenvalue decomposition
• Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values
• Estimating the cluster tree of a density by analyzing the minimal spanning tree of a sample
• Graphical tools for model-based mixture discriminant analysis
• Maximum likelihood estimation for multivariate skew normal mixture models
• The EM Algorithm and Extensions, 2E
• Introduction to Semi-Supervised Learning
• Model Selection in High Dimensions: A Quadratic-Risk-Based Approach
• On the Use of Discriminant Functions in Taxonomy
• The estimation of the gradient of a density function, with applications in pattern recognition
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Practical Bayesian Density Estimation Using Mixtures of Normals
• The quickhull algorithm for convex hulls
• Finite mixture models
• Variable Selection for Model-Based Clustering

This page was built for publication: Identifying connected components in Gaussian finite mixture models for clustering