Using evolutionary algorithms for model-based clustering

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.

► We introduce evolutionary algorithms as a robust alternative to the EM algorithm.
► The algorithms mutate the component indicator variables for model-based clustering.
► These algorithms perform favourably with respect to the log-likelihood.