Abstract
In this paper, we deal with a particular type of information, namely interval-valued data. We face the problem of clustering data units described by intervals of the real data set (interval data). Currently, clustering methods rely on dissimilarity measures for interval-valued data uses representative point distance. The Data Group Average Clustering UPGMA is one of the popular algorithms to construct a phylogenetic tree according to the distance matrix created by the pairwise distances among taxa. A phylogenetic tree is used to present the evolutionary relationships among the interesting biological species based on the similarities in their genetic sequences. Interval-valued Data Group Average Clustering (IUPGMA) extends Group Average 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. They contain more information than point-valued data, and such informational advantages could be exploited to yield more efficient analysis.
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