Interval-valued Data Ward's Minimum Variance Clustering - Centroid update Formula
Jornandes Dias, Sérgio Mário Lins Galdino · 2021 International Conference on Engineering and Emerging Technologies (ICEET) · 2021
Interval-valued Data Ward's Minimum Variance Clustering is one of several methods of agglomerative hierarchical clustering. It is a very popular method for computing hierarchical clusterings. Currently, clustering methods rely dissimilarity measures for interval-valued data uses representative point distance. Our work extend Ward's clustering to interval-valued data. Based on the Range Euclidean Metric it is a reliable alternative to be used to uncertainty quantification from interval-valued data.