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An artificial bee colony algorithm for mixture model-based clustering Culos, Anthony E.; Andrews, Jeffrey L.; Afshari, Hamid

Abstract

Finite mixture model-based clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectationmaximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarm-based algorithms have gained popularity in a number of optimization scenarios. One such swarm-based optimization procedure, the artificial bee colony, incorporates both local searching and multiple solution generating to explore the parameter space. Herein, we propose hybridizing these algorithms for the task of fitting a multivariate Gaussian mixture model. This algorithm’s performance is contrasted with both the traditional approach and another hybrid algorithm on simulated and real data.

Item Metadata

Title
An artificial bee colony algorithm for mixture model-based clustering
Creator
Culos, Anthony E.; Andrews, Jeffrey L.; Afshari, Hamid
Date Issued
2020-07
Description
Finite mixture model-based clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectationmaximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarm-based algorithms have gained popularity in a number of optimization scenarios. One such swarm-based optimization procedure, the artificial bee colony, incorporates both local searching and multiple solution generating to explore the parameter space. Herein, we propose hybridizing these algorithms for the task of fitting a multivariate Gaussian mixture model. This algorithm’s performance is contrasted with both the traditional approach and another hybrid algorithm on simulated and real data.
Subject
Cluster analysis; EM algorithm; Swarm intelligence; Finite mixture models; Model-based clustering; Artificial bee colony
Genre
Article; Postprint
Type
Text
Language
eng
Date Available
2025-03-05
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
DOI
10.14288/1.0448168
URI
http://hdl.handle.net/2429/90461
Affiliation
Science, Irving K. Barber Faculty of (Okanagan); Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
Citation
Culos, A. E., Andrews, J. L., & Afshari, H. (2020). An artificial bee colony algorithm for mixture model-based clustering. Communications in StatisticsSimulation and Computation, 51(10), 5658-5669
Publisher DOI
10.1080/03610918.2020.1779291
Peer Review Status
Reviewed
Scholarly Level
Faculty; Postdoctoral; Undergraduate
Rights URI
http://creativecommons.org/licenses/by-nc-nd/4.0/
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Attribution-NonCommercial-NoDerivatives 4.0 International