New Clustering methods for interval data
Idioma
en
Article de revue
Este ítem está publicado en
Computational Statistics. 2006, vol. 21, p. 211-229
Springer Verlag
Resumen en inglés
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). ...Leer más >
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.< Leer menos
Palabras clave en italiano
Dynamic clustering
interval data
distances
prototypes
Orígen
Importado de HalCentros de investigación