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 …

This paper presents a partitional dynamic clustering method for interval data based on
adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step …

This paper presents partitioning dynamic clustering methods for interval-valued data based
on suitable adaptive quadratic distances. These methods furnish a partition and a prototype …

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 …

Dynamic cluster methods for interval data are presented. Two methods are considered: the
first method furnishes a partition of the input data and a corresponding prototype (a vector of …

In this paper we propose two clustering methods for interval data based on the dynamic
cluster algorithm. These methods use different homogeneity criteria as well as different kinds …

The recording of interval data has become a common practice with the recent advances in
database technologies. This paper introduces clustering methods for interval data based on …

This paper introduces a partitioning clustering method for objects described by interval data.
It follows the dynamic clustering approach and uses and L 2 distance. Particular emphasis is …

This paper presents a clustering method for interval-valued data using a dynamic cluster
algorithm with adaptive squared Euclidean distances. This method furnishes a partition and …

Clustering methods are becoming key as analysts try to understand what knowledge is
buried inside contemporary large data sets. This article analyzes the impact of six different …