Using evolutionary algorithms for model-based clustering

doi:10.1016/j.patrec.2013.02.008

Using evolutionary algorithms for model-based clustering

2013-07-01

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DOI：10.1016/j.patrec.2013.02.008

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In mixture model-based clustering, parameter estimation is generally carried out using the expectation-maximization algorithm, or some closely related variant. We present a new approach by casting the model-fitting problem as a single-objective evolutionary algorithm that focuses on searching the cluster-membership space. The appeal of an evolutionary algorithm is its ability to more thoroughly search the parameter space, providing an approach inherently more robust with respect to local maxima. This approach is illustrated through application to both simulated and real clustering data sets where comparisons are drawn with traditional model-fitting algorithms. (C) 2013 Elsevier B.V. All rights reserved.

Keywords:

Cluster analysis

EM algorithm

Evolutionary algorithms

Finite mixture models

Model-based clustering

Using evolutionary algorithms for model-based clustering

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