Fuzzy clustering is an alternative method to conventional or hard clustering algorithms,
which makes partitions of data containing similar subjects. The tendency of adopting …

Information fusion is capable of fusing and transforming information originated from multiple
sources into an integrated representation. As an important representative of information …

This paper introduces a new approach to fitting a linear regression model to symbolic
interval data. Each example of the learning set is described by a feature vector, for which …

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 …

Incremental learning strategy based feature selection approaches can improve the efficiency
of reduction algorithm used for datasets with dynamic characteristic, which has attracted …

Interval-valued data describe the random phenomenon that abounds in the real world, a
pivotal research orientation in uncertainty processing. With the rapid development of big …

Interval-valued time series are interval-valued data that are collected in a chronological
sequence over time. This paper introduces three approaches to forecasting interval-valued …

In this paper, we propose a new method of representing on-line signatures by interval
valued symbolic features. Global features of on-line signatures are used to form an interval …

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 …

Clustering algorithms create groups of objects based on their similarity. As objects are
usually defined by data points, this similarity is commonly measured by a distance function …