Using evolutionary algorithms for model-based clustering
In mixture model-based clustering, parameter estimation is generally carried out using the expectationmaximization 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.
evolutionary algorithms model-based clustering cluster analysis em algorithm finite mixture models