An effective strategy for initializing the EM algorithm in finite mixture models

DOI10.1007/S11634-016-0264-8zbMath1414.62256OpenAlexW2478992108MaRDI QIDQ2418282

Semhar Michael, Volodymyr Melnykov

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-016-0264-8

zbMATH Keywords

EM algorithmBICmodel averagingfinite mixture modelsinitialization

Mathematics Subject Classification ID

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

Related Items (3)

Finite mixture of regression models for a stratified sample ⋮ Mixture modeling of data with multiple partial right-censoring levels ⋮ Matrix normal cluster-weighted models

Uses Software

• mclust
• mixsmsn
• MixSim
• Rmixmod
• EMCluster

Cites Work

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• Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
• Methods for merging Gaussian mixture components
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Model-based clustering of high-dimensional data: a review
• Semi-supervised model-based clustering with positive and negative constraints
• Studying Complexity of Model-based Clustering
• Finding Groups in Data
• Algorithms for Model-Based Gaussian Hierarchical Clustering
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• The multivariate skew-normal distribution
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finite mixture models

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