An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

Sharon M. McNicholas (), Paul D. McNicholas () and Daniel A. Ashlock ()
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Sharon M. McNicholas: McMaster University
Paul D. McNicholas: McMaster University
Daniel A. Ashlock: University of Guelph

Journal of Classification, 2021, vol. 38, issue 2, No 6, 264-279

Abstract: Abstract An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approach to “hard” model-based clustering and so it can be viewed as a sort of generalization of the k-means algorithm, which is itself equivalent to a restricted Gaussian mixture model. The EA is illustrated on several datasets, and its performance is compared with that of other hard clustering approaches and model-based clustering via the EM algorithm.

Keywords: Clustering; Crossover; Evolutionary algorithm; Mixture models; Mutation; Model-based clustering (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00357-020-09371-4

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