In fuzzy c-means (FCM) clustering algorithm, each data point belongs to a cluster with a degree specified by a membership grade. Furthermore, FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group so that the objective function is minimized. This paper introduces a clustering method for objects described by interval data. It extends the FCM clustering algorithm by using combined distances. Moreover, simulated experiments with interval data sets have been performed in order to show the usefulness of this method. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Gu, S. M., Zhao, J. W., & He, L. (2010). An improved FCM clustering method for interval data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6401 LNAI, pp. 545–550). https://doi.org/10.1007/978-3-642-16248-0_74
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