Wasserstein Metric Based Adaptive Fuzzy Clustering Methods for Symbolic Data
نویسنده
چکیده
Given the current limitations in fuzzy clustering metric, the aim of this paper is to present new wasserstein metric based adaptive fuzzy clustering methods for partitioning symbolic interval data. Wasserstein metric shows adavantages in digging distribution information in symbolic interval data. Besides, the proposed fuzzy clustering methods also emphasize correlation structure between indices. Based on it, fuzzy partitions and prototypes for clusters are determined by optimizing adequacy criteria. Finally, the applicability and effectiveness of the proposed methods are validated through experiments with synthetic data sets.
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