Wasserstein Metric Based Adaptive Fuzzy Clustering Methods for Symbolic Interval Data

Abstract

The aim of this paper is to present new wasserstein metric based adaptive fuzzy clustering methods for partitioning symbolic interval data. In two methods, fuzzy partitions and prototypes for clusters are determined by optimizing adequacy criteria based on wasserstein distances between vectors of intervals. The applicability and effectiveness of the proposed methods are validated through experiments with synthetic data sets. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3630