Abstract
The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy c-means clustering algorithm for symbolic interval data. This 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 a suitable single adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.
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de A.T. de Carvalho, F. (2006). A Fuzzy Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Euclidean Distance. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_111
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DOI: https://doi.org/10.1007/11893295_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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