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

Abstract Chavent's [1-2] monothetic divisive clustering algorithm has been used extensively
for clustering interval-valued observations. To overcome some limitations of this algorithm …

Este trabalho relata sobre os diferentes métodos de agrupamento presentes na literatura
atual e introduz métodos de agrupamento baseado na abordagem possibilística para dados …

Data may be afflicted with uncertainty. Uncertain data may be shown by an interval value or
in general by a fuzzy set. A number of classification methods have considered uncertainty in …

In this paper a new statistical measure for estimating the degree of dissimilarity between two
symbolic objects whose features are multivalued symbolic data type is proposed. In addition …

Interval-valued data are one of the most common forms of symbolic data. Previous studies
have provided a number of approaches to conduct linear regression models for interval …

Interval data is attracting attention from the data analysis community due to its ability to
describe complex concepts. Since clustering is an important data analysis tool, extending …

In this paper, we have introduced two different ways of approximation of symbolic proximity
between the objects. The proximity between the objects is represented by the use of data of …

In this study, the novel robust clustering algorithm, robust interval competitive agglomeration
(RICA) clustering algorithm, is proposed to overcome the problems of the outliers and the …

With the rapid development of cross-platform data collection technology and the coming of
the big data era, there are always a mixture of single-valued data, symbolic data …