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- W3048364984 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." @default.
- W3048364984 created "2020-08-18" @default.
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- W3048364984 date "2020-08-12" @default.
- W3048364984 modified "2023-09-27" @default.
- W3048364984 title "An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering" @default.
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- W3048364984 doi "https://doi.org/10.1007/s00357-020-09371-4" @default.
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