Data clustering has emerged as a prospective technique for analyzing interval-valued data
and has found extensive applications across various practical domains. However, the …

Correlation-based hierarchical clustering methods for time series typically are based on a
suitable dissimilarity matrix derived from pairwise measures of association. Here, this …

This paper analyzes the behavior of the well-known Spearman's footrule distance (F-
distance) to measure the distance between two rankings over the same set of objects. We …

With the development of fuzzy set theory, the clustering methods widely used in various
fields can be divided into hard clustering methods and fuzzy clustering methods. Fuzzy …

Interval-valued data has garnered attention across various applications, leading to
increased research into spatial interval-valued data models. The integration of uncertainty …

A fuzzy clustering model for data with mixed features and spatial constraints is proposed.
The clustering model allows different types of variables, or attributes, to be taken into …

In this paper, we propose a new algorithm for the joint clustering of two sets of statistical
units\(\mathcal {N}\) and\(\mathcal {M}\) which are also equipped with an adjacency structure …

In this paper, we investigate similarities of suicide rates in Europe, which are available as
interval time series. For this aim, a novel spatio-temporal hierarchical clustering algorithm for …