For several years, parts of the content of this textbook have been used in undergraduate
courses in the Department of Mathematics and in the Faculty of Economics at the University …

In this paper, we introduce a fuzzy extension of a class of measures to compare clustering
structures, namely, measures that are based on the number of concordant and the number …

In this article, we propose a general framework for the development of external evaluation
measures for soft clustering. Our proposal is based on the interpretation of soft clustering as …

This paper introduces hard clustering algorithms that are able to partition objects taking into
account simultaneously their relational descriptions given by multiple dissimilarity matrices …

The well known k-medoids clustering approach groups objects through finding k
representative objects based on the pairwise (dis) similarities of objects in the data set. In …

The conventional Gaussian kernel fuzzy c-means clustering algorithms require selecting the
width hyper-parameter, which is data-dependent and fixed for the entire execution. Not only …

In this article, we propose and study a general framework for comparing and evaluating soft
clusterings, viewed as a form of uncertainty quantification for clustering tasks, with the aim of …

In many applications, entities of the domain are described through different views that
clustering methods often process one by one. We introduce here the architecture MVSim …

In evidential clustering, cluster-membership uncertainty is represented by Dempster–Shafer
mass functions. The notion of evidential partition generalizes other soft clustering structures …

The development of reliable mathematical models for nonlinear systems has been a primary
topic in several industrial applications. This work proposes to examine the application of …