Interval-valued Data Clustering based on the Range City Block metric
Sérgio Mário Lins Galdino · 2016
This paper introduces a new approach to Data Clustering on interval-valued data. Nowadays dissimilarity measures for interval-valued data uses representative point distance. It was defined the Range City Block metric. Interval-valued input distance matrix is used to process hierarchical clustering by single linkage with partial ordering. The new method based on the Range City Block Metric can allows for a better analysis of the clustering results on interval-valued data.