Resumen

AGARDI, A.  y  KOVACS, L.. Clustering algorithms with prediction of the optimal number of clusters. J. appl. res. technol [online]. 2022, vol.20, n.6, pp.638-651.  Epub 08-Mayo-2023. ISSN 2448-6736.  https://doi.org/10.22201/icat.24486736e.2022.20.6.1077.

Clustering is a widely used technique for grouping of objects. The objects, which are similar to each other, should be in the same cluster. One disadvantage of general clustering algorithms is that the user must specify the number of clusters in advance, as input parameter. This is a major drawback since it is possible that the user cannot specify the number of clusters correctly, and the algorithm thus creates a clustering that puts very different elements into the same cluster. The aim of this paper is to present our representation and evaluation technique to determine the optimal cluster count automatically. With this technique, the algorithms themselves determine the number of clusters. In this paper, first, the classical clustering algorithms are introduced; then, the construction and improvement algorithms and then our representation and evaluation method are presented. Then the performance of the algorithms with the test results are compared.

Palabras llave : clustering; optimal number of clusters.

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