Abstract
The applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are categorized into centroid and non-centroid type of functions. Optimization of the centroid type of objective functions is accomplished by formulating them as functions of real-valued parameters using ESs. Both hard and fuzzy clustering objective functions are considered in this study. Applicability of ESs to discrete optimization problems is extended to optimize the non-centroid type of objective functions. As ESs are amenable to parallelization, a parallel model (master/slave model) is described in the context of the clustering problem. Results obtained for selected data sets substantiate the utility of ESs in clustering.
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