Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such
data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering …

In several real life and research situations data are collected in the form of intervals, the so
called interval-valued data. In this paper a fuzzy clustering method to analyse interval …

In this paper, following a partitioning around medoids approach, a fuzzy clustering model for
interval-valued data, ie, FCMd-ID, is introduced. Successively, for avoiding the disruptive …

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, ie
the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy …

In many real cases the data are not expressed in term of single values but are imprecise. In
all these cases, standard clustering methods for single-valued data are unable to properly …

Interval-valued data arises in situations where it is needed to manage either the uncertainty
related to measurements, or the variability inherent to a group rather than an individual. This …

Clustering for symbolic data type is a necessary process in many scientific disciplines, and
the fuzzy c-means clustering for interval data type (IFCM) is one of the most popular …

In recent years, the research of statistical methods to analyze complex structures of data has
increased. In particular, a lot of attention has been focused on the interval-valued data. In a …

Fuzzy clustering helps to find natural vague boundaries in data. The Fuzzy C-Means method
(FCM) is one of the most popular clustering methods based on minimization of a criterion …

Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-
means method is one of the most popular clustering methods based on minimization of a …