7th International Conference on Hybrid Intelligent Systems (HIS 2007) A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances Kaiserslautern, Germany September 17-September 19 ISBN: 0-7695-2946-1
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2007.33
The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a fuzzy clustering algorithm to partitioning symbolic interval data. The proposed method furnish a fuzzy partition and a prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare symbolic interval data, the method use a suitable adaptive Mahalanobis disance defined on vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.
Citation:
Camilo P. Tenorio, Francisco de A.T. de Carvalho, Julio T. Pimentel, "A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances," his, pp.174-179, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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