New Clustering methods for interval data
Language
en
Article de revue
This item was published in
Computational Statistics. 2006, vol. 21, p. 211-229
Springer Verlag
English Abstract
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 of cluster representations (prototypes). ...Read more >
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 of cluster representations (prototypes). Some tools to interpret the final partitions are also introduced. An application of one of the methods concludes the paper.Read less <
Italian Keywords
Dynamic clustering
interval data
distances
prototypes
Origin
Hal imported