Abstract
In this paper we introduce a new principle for two classical problems in clustering: obtaining a set of partial classes and a partition on a set X of n elements. These structures are built from a distance D and a threshold value σ giving a threshold graph on X with maximum degree δ. The method is based on a density function De : X → R which is computed first from D. Then, the number of classes, the classes, and the partitions are established using only this density function and the graph edges, with a computational complexity of o(nδ). Monte Carlo simulations, from random Euclidian distances, validate the method.
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© 2004 Springer-Verlag Berlin Heidelberg
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Guénoche, A. (2004). Clustering by Vertex Density in a Graph. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_2
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DOI: https://doi.org/10.1007/978-3-642-17103-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22014-5
Online ISBN: 978-3-642-17103-1
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