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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Vydáno v:Pattern recognition Ročník 39; číslo 5; s. 776 - 788
Hlavní autoři: Brás Silva, Helena, Brito, Paula, Pinto da Costa, Joaquim
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2006
Témata:
Clustering algorithms
Clustering validity
Unsupervised learning
Clustering validity
Clustering algorithms
Unsupervised learning
ISSN:0031-3203, 1873-5142
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Shrnutí: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