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Journal of Classification
Erschienen in: Journal of Classification 2/2021

12.08.2020

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

verfasst von: Sharon M. McNicholas, Paul D. McNicholas, Daniel A. Ashlock

Erschienen in: Journal of Classification | Ausgabe 2/2021

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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.
Vorheriger Artikel Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals
Nächster Artikel Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data

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Metadaten
Titel
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering
verfasst von
Sharon M. McNicholas
Paul D. McNicholas
Daniel A. Ashlock
Publikationsdatum
12.08.2020
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 2/2021
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-020-09371-4

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