A partitional clustering algorithm validated by a clustering tendency index based on graph theory
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摘要
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.
论文关键词：Unsupervised learning,Clustering algorithms,Clustering validity
论文评审过程：Received 5 November 2004, Revised 14 October 2005, Accepted 14 October 2005, Available online 3 February 2006.
论文官网地址：https://doi.org/10.1016/j.patcog.2005.10.027
