Abstract
The fundamental issue of data clustering is an evaluation of results of clustering algorithms. Lots of methods have been proposed for cluster validation. The most popular approach is based on internal cluster validity indices. Among this kind of indices, the Silhouette index and its computationally simpled version, i.e. the Simplified Silhouette, are frequently used. In this paper modification of the Simplified Silhouette index is proposed. The suggested approach is based on using an additional component, which improves clusters validity assessment. The performance of the new cluster validity indices has been demonstrated for artificial and real datasets, where the PAM clustering algorithm has been applied as the underlying clustering technique.
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Starczewski, A., Przybyszewski, K. (2018). Improvement of the Simplified Silhouette Validity Index. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_39
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