Abstract
Finding a data clustering in a data set is a challenging task since algorithms usually depend on the adopted inter-cluster distance as well as the employed definition of cluster diameter. The work described in this paper approaches a well-known agglomerative clustering algorithm named AGNES (Agglomerative Nesting), in regards to its performance on three case studies namely, datasets formed by clusters of different sizes, uneven inter-cluster distances and diameters. Clustering results are evaluated using three well-known indexes, Dunn, Davies-Bouldin and Rand. Results obtained with K-means were used for comparison purposes. The experiments were conducted divided into three case studies. Their results suggest that AGNES and K-means have similar performance as far as identifying clusters with different sizes and inter-cluster distances, however, AGNES obtained the best results when dealing with clusters having both, different sizes and diameters.
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The authors would like to thank CAPES, CNPq and FACCAMP.
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Camargos, R.C., do Carmo Nicoletti, M. (2017). Three Case Studies Using Agglomerative Clustering. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_7
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DOI: https://doi.org/10.1007/978-3-319-53480-0_7
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