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Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions and it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution for automatic clustering using the Calinski-Harabasz Index as a form of evaluation. Comparisons between the results and other algorithms in literature are also presented. In a first analysis, fitness values equivalent or higher are found in at least 58% of the cases for each comparison. Our algorithm could also find the correct number of clusters or close values in 33 cases out of 48. In another comparison, some fitness values are lower, even with the correct number of clusters, but graphically the partitioning are adequate. Thus, it is observed that our proposal is justified and that improvements can be studied for cases in which the correct number of clusters is not found.

A genetic algorithm using Calinski-Harabasz index for automatic clustering problem