Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm

Javier Arellano-Verdejo, Adolfo Guzmán-Arenas, Salvador Godoy-Calderon, Ricardo Barrón Fernández


A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas; on the other hand, clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. Some existing methods use evolutionary algorithms with cluster validation index as the objective function. In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets and indexes.


Clustering; cellular genetic algorithm; microevolutionary algorithms; particle swarm optimization; optimal number of clusters

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