Evaluation of Clustering Algorithm with Cluster Validation Metrics
Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms are designed to find clusters that fit some static models. In this paper, the authors are going to evaluate the performance of some of the popular clustering algorithms chameleon, DBSCAN, FC-Mean and K-means algorithm. Clustering in general, the quality of the discovered clusters are validated using suitable cluster validation metrics. The performance of the algorithms was tested with synthetic as well as real datasets using two cluster validation metrics; the Generalized Dunn Index and the Davies-Bouldin Index.