Abstract
This paper presents a clustering method for interval-valued data using a dynamic cluster algorithm with adaptive squared Euclidean distances. This method furnishes a partition and a prototype to each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare a class with its representative, the method uses an adaptive version of a squared Euclidean distance to interval-valued data. Experiments with real and artificial interval-valued data sets shows the usefulness of the this method.
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References
Bock, H.H., Diday, E.: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)
De Carvalho, F.A.T., Brito, P., Bock, H.H.: Dynamical Clustering for symbolic quantitative data. In: Workshop in Symbolic Data Analysis, Cracow (2002)
Diday, E., Govaert, G.: Classification Automatique avec Distances Adaptatives. R.A.I.R.O. Informatique Computer Science 11(4), 329–349 (1977)
Hubert, L., Arabie, P.: Comparing Partitions. Journal of Classification 2, 193–218 (1985)
Souza, R.M.C.R., De Carvalho, F.A.T.: Clustering of interval data based on city-block distances. Pattern Recognition Letters 25(3), 353–365 (2004)
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de Souza, R.M.C.R., de A.T. de Carvalho, F., Silva, F.C.D. (2004). Clustering of Interval-Valued Data Using Adaptive Squared Euclidean Distances. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_119
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DOI: https://doi.org/10.1007/978-3-540-30499-9_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
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