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An evolutionary algorithm with crossover and mutation for model-based clustering. (English) Zbl 07413948

Summary: 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.

MSC:

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

Software:

R; mixture; mclust; Flury
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References:

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