Abstract
Amid the variety of clustering algorithms and the different types of obtainable partitions on the same dataset, a framework that generalizes and explains the aspects of the clustering problem has become necessary. This study casts the problem of clustering a given set of data points as a problem of clustering the associated pairwise distances, thereby capturing the essence of the common definition of clustering found in literature. The main goal is to obtain a general cluster validity index, in particular, to generalize the average silhouette index to fuzzy partitions.
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Rawashdeh, M., Ralescu, A. (2012). A Pairwise Distance View of Cluster Validity. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_57
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DOI: https://doi.org/10.1007/978-3-642-31709-5_57
Publisher Name: Springer, Berlin, Heidelberg
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