It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters. In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed. This index can cope with both the spherical and non-spherical structure of clusters. The experimental results indicate that the proposed index outperforms some classical CVIs.INDEX TERMS Crisp clustering, cluster validity index, arbitrary-shaped clusters.
Highlights d We propose an influential node detection method, TARank, in a graph-traversal framework d We evaluate the influence of each node by constructing a breadth-first search tree d TARank is capable of enhancing existing centrality measures d TARank can yield new, yet effective, centrality measures as well
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