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 clustering tendency index value.
clustering algorithms unsupervised learning clustering validity