Partitional Cluster Analysis with Genetic Algorithms: Searching for the Number of Clusters

In this article we deal with the problem of searching for the number of clusters in partitional clustering in R 2. We set up the problem as an optimization problem by giving a real function on the different partitions that is optimized when the number of clusters and the classes are the most natural. We use the Genetic Algorithm for optimizing this function. The algorithm has been applied to the well-known Ruspini data and to synthetic cally generated datasets, with different cluster numbers and underlying distributions. The results are encouraging.

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