|
|||||
|
|
New clustering methods for interval data |
|
| Authors: | Marie Chavent Francisco de A T de Carvalho Yves Lechevallier Rosanna Verde |
| Institution: | 1. MAB-Mathématiques Appliquées de Bordeaux, Université Bordeaux1, 351 cours de la libération, 33405, Talence cedex, France 2. CIn-Centro de Informática UFPE-Universidade Federal de Pernambuco, Av. Prof. Luiz Freire s/n-Cidade Universitária, CEP 50740-540, Recife-PE, Brasil 3. INRIA-Institut National de Recherche en Informatique et en Automatique, Domaine de Voluceau, Rocquencourt B.P. 105, 78153, Le Chesnay Cedex, France 4. Dip. Strategie Aziendali e Metodologie Quantitative, SUN-Seconda Università di Napoli, Corso Gran Priorato di Malta, 81043, Capua, Italie |
| Abstract: | Summary 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. |
| Keywords: | Dynamic clustering interval data distances prototypes |
| 本文献已被 SpringerLink 等数据库收录！ | |