Wasserstein Metric Based Adaptive Fuzzy Clustering Methods for Symbolic Interval Data

LI HONG

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


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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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