RNTI

MODULAD
Adaptive Dynamic Clustering Algorithm for Interval-valued Data based on Squared-Wasserstein Distance
In HDSDA 2013, vol. RNTI-E-25, pp.15-30
Abstract
Wide applications of interval-valued data in various domains have triggered the call for more powerful analytical tools. In light of this, this paper has presented an adaptive dynamic clustering algorithm for interval-valued data, using squared-Wasserstein distance. Experiments on both synthetic data and real data have unveiled the merits of the proposed algorithm.