New clustering methods for interval data

Authors:
Marie Chavent;Francisco De Carvalho;Yves Lechevallier;Rosanna Verde
Affiliations:
MAB-Mathématiques Appliquées de Bordeaux, Université Bordeaux1, Talence cedex, France 33405;CIn-Centro de Informática UFPE-Universidade Federal de Pernambuco, Recife-PE, Brasil;INRIA-Institut National de Recherche en Informatique et en Automatique, Le Chesnay Cedex, France 78153;Dip. Strategie Aziendali e Metodologie Quantitative, SUN-Seconda Università di Napoli, Capua, Italie 81043
Venue:
Computational Statistics
Year:
2006

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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). Some tools to interpret the final partitions are also introduced. An application of one of the methods concludes the paper.