An artificial bee colony algorithm for mixture model-based clustering

DOI10.1080/03610918.2020.1779291OpenAlexW3039573145WikidataQ113279020 ScholiaQ113279020MaRDI QIDQ5042160

Jeffrey L. Andrews, Hamid Afshari, Anthony E. Culos

Publication date: 18 October 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2020.1779291

zbMATH Keywords

EM algorithmmodel-based clusteringcluster analysisswarm intelligencefinite mixture modelsartificial bee colony

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• ABC

Cites Work

• Unnamed Item
• Model-based clustering, classification, and discriminant analysis via mixtures of multivariate \(t\)-distributions
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Choosing initial values for the EM algorithm for finite mixtures
• Estimating the dimension of a model
• Parsimonious skew mixture models for model-based clustering and classification
• Addressing overfitting and underfitting in Gaussian model-based clustering
• A comprehensive survey on particle swarm optimization algorithm and its applications
• Improved initialisation of model-based clustering using Gaussian hierarchical partitions
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• The EM Algorithm and Extensions, 2E
• Statistical analysis of finite mixture distributions
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation

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