Abstract
This work presents a partitioning method for clustering symbolic interval-type data using a dynamic cluster algorithm with adaptive Chebyshev distances. This method furnishes a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare interval-type data, the method uses an adaptive Chebyshev distance that changes for each cluster according to its intra-class structure at each iteration of the algorithm. Experiments with real and artificial interval-type data sets demonstrate the usefulness of the proposed method.
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© 2004 Springer-Verlag Berlin Heidelberg
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de A.T. de Carvalho, F., de Souza, R.M.C.R., Silva, F.C.D. (2004). A Clustering Method for Symbolic Interval-Type Data Using Adaptive Chebyshev Distances. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_27
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DOI: https://doi.org/10.1007/978-3-540-28645-5_27
Publisher Name: Springer, Berlin, Heidelberg
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