This paper introduces an approach to fitting a constrained linear regression model to interval-
valued data. Each example of the learning set is described by a feature vector for which …

This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for
partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and …

This paper introduces dynamic clustering methods for partitioning symbolic interval data.
These methods furnish a partition and a prototype for each cluster by optimizing an …

Clustering categorical data is an integral part of data mining and has attracted much
attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering …

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 this paper, we present a method for representing a two-dimensional shape by symbolic
features. A shape is represented in terms of multi-interval valued type features. A similarity …

Unsupervised pattern recognition methods for mixed feature-type symbolic data based on
dynamical clustering methodology with adaptive distances are presented. These distances …

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 …

This paper suggests a fuzzy inference system (iFIS) modeling approach for interval-valued
time series forecasting. Interval-valued data arise quite naturally in many situations in which …

Gait is one of the biometric techniques used to identify an individual from a distance by
his/her walking style. Gait can be recognized by studying the static and dynamic part …