A kernel k-means clustering method for symbolic interval data


Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been… (More)
DOI: 10.1109/IJCNN.2010.5596801


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