A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Deterministic annealing EM algorithm
Neural Networks
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
Computational Statistics & Data Analysis
Initializing EM using the properties of its trajectories in Gaussian mixtures
Statistics and Computing
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parsimonious Gaussian mixture models
Statistics and Computing
Model-based clustering with non-elliptically contoured distributions
Statistics and Computing
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization
Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.