A novel internal cluster validity index
Shibing Zhou, Fei Liu · Journal of Intelligent & Fuzzy Systems · 2020
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.