Affiliations: [a] Department of Computer Science and Technology, Jiangnan University, Wuxi, Jiangsu, P.R. China | [b] Jiangsu Provincial Engineering Laboratory for Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, Jiangsu, P.R. China | [c] Institute of Automation, Jiangnan University, Wuxi, Jiangsu, P.R. China
Correspondence:
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Corresponding author. Shibing Zhou, Department of Computer Science and Technology, Jiangnan University, Wuxi, Jiangsu, P.R. China. Tel.: +86 510 85910652; Fax: +86 510 85912085; E-mail: worldguard@sina.com.
Abstract: It is critical to determine the optimal number of clusters (NC) in cluster analysis. Many cluster validity indices have been proposed, such as the Silhouette index and In-group proportion index. However, these validity indices have more time complexity. From the viewpoint of sample geometry, a new internal cluster validity index for determining the optimal NC is proposed. The new index can evaluate the clustering quality of a certain clustering algorithm and determine the optimal NC for many kinds of data sets, including synthetic data sets, benchmark data sets, and real data sets. Compared with many well-known validity indices, the proposed index is more effective and efficient. Theoretical analysis and experimental results show the effectiveness and high efficiency of the new index.
Keywords: Cluster validity index, number of clusters, affinity propagation, hierarchical clustering
