Abstract
The paper addresses the problem of assessing the validity of the clusters found by a clustering algorithm. The determination of the “true” number of “natural” clusters has often been considered as the central problem of cluster validation. Many different stopping rules have been proposed in the research literature but most of them are applicable only to classical data (qualitative or quantitative). In this paper we investigate the problem of the determination of the number of clusters for symbolic objects described by interval variables. We consider five classical methods and two hypothesis tests based on the Poisson point process. We extend these methods to interval data. We apply them to the meteorological stations data set.
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Hardy, A., Baune, J. (2007). Clustering and Validation of Interval Data. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_7
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DOI: https://doi.org/10.1007/978-3-540-73560-1_7
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