A partitional clustering algorithm validated by a clustering tendency index based on graph theory

Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clusteri...

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Veröffentlicht in:Pattern recognition Jg. 39; H. 5; S. 776 - 788
Hauptverfasser: Brás Silva, Helena, Brito, Paula, Pinto da Costa, Joaquim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2006
Schlagworte:
Clustering algorithms
Clustering validity
Unsupervised learning
Clustering validity
Clustering algorithms
Unsupervised learning
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
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Zusammenfassung:Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clustering tendency index value.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2005.10.027