Abstract
Dynamic cluster methods for interval data are presented. Two methods are considered: the first method furnishes a partition of the input data and a corresponding prototype (a vector of intervals) for each class by optimizing an adequacy criterion which is based on Mahalanobis distances between vectors of intervals. The second is an adaptive version of the first method. Experimental results with artificial interval-valued data sets show the usefulness of these methods. In general, the adaptive method outperforms the non-adaptive one in terms of the quality of the clusters which are produced by the algorithms.
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de Souza, R.M., de A. T. de Carvalho, F., Tenório, C.P., Lechevallier, Y. (2004). Dynamic Cluster Methods for Interval Data Based on Mahalanobis Distances. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_34
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DOI: https://doi.org/10.1007/978-3-642-17103-1_34
Publisher Name: Springer, Berlin, Heidelberg
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