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By Marie Chavent, Rosanna Verde, Pernambuco Av, Prof Luiz, Freire Cidade Universitária and Le Chesnay Cedex

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

Topics: Dynamic clustering, interval data, distances, prototypes
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.371.7169
Provided by: CiteSeerX
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