Differential Evolution with Competing Strategies Applied to Partitional Clustering
نویسندگان
چکیده
We consider the problem of optimal partitional clustering of real data sets by optimizing three basic criteria (trace of within scatter matrix, variance ratio criterion, and Marriottt’s criterion). Four variants of the algorithm based on differential evolution with competing strategies are compared on eight real-world data sets. The experimental results showed that hybrid variants with k-means algorithm for a local search are essentially more efficient than the others. However, the use of Marriottt’s criterion resulted in stopping hybrid variants at a local minimum.
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Differential evolution and particle swarm optimisation in partitional clustering
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