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Since drug-target data have neither class labels nor the cluster number information, they are not suitable for clustering algorithms that require predefined parameters determined by comparing clustering results with real class labels. Density peaks clustering (DPC) is a density-based clustering algorithm that can determine the number of clusters without requiring class labels. However, the predefined cutoff distance of local density limits its wide application. Therefore, this paper proposes an improved local density method based on a cutoff distance sequence that overcomes the limitations of DPC and can be successful applied to drug-target data. We also introduce multiple-dimensional scaling based on drug and target similarity and perform intuitive graph analysis of the two most significant differentiation features. Drugs of the Enzyme, GPCR, Ion Channel, and Nuclear Receptor 4 standard datasets are identified as 6, 6, 3, and 5 clusters by an improved algorithm, respectively, and similarly, their targets are identified be 5, 5, 8, and 4 clusters. Drug-target data clustering results of the improved algorithm are more reasonable than the results of the fast K-medoids and hierarchical clustering algorithms. [ABSTRACT FROM AUTHOR]

Clustering analysis is a powerful tool in customer segmentation. Although various algorithms have been proposed, the determination of the optimal number of clusters remains to be a difficult issue. In this paper, a clustering method based on consistency criterion is proposed to address this issue. The main characteristic of the new approach is that it requires little prior information and can find the optimal number of clusters automatically. Extensive comparisons are done over 22 real-world datasets from different domains, in which four well-known clustering algorithms in combination with six clustering indices are used as the benchmark methods. The results demonstrate the superiority of our method in appropriately determining the number of clusters. An application of the new approach in customer segmentation of credit card users is also illustrated. [ABSTRACT FROM AUTHOR]