Abstract
Clustering by fast search and find of density peaks is a new density-based clustering algorithm, which is widely used in various fields owing to its simplicity and efficiency, unique parameters, and recognition of arbitrary shape clusters. However, when selecting the cluster center requires human participation, which makes the clustering result to be subjectively affected by the operator, thus reducing the availability of clustering and interrupting the fluency of the algorithm. In this study, to eliminate artificial participation in the selection of cluster centers, a weighted decision measurement slope change method is proposed to select cluster centers, and the F-Measure, ARI, and AMI of the algorithm are tested in the UCI and synthetic datasets. Experimental results show that the proposed algorithm addresses the limitation of human participation in the selection of cluster centers and improves the clustering performance of the algorithm.
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This work was supported by the National Natural Science Foundation of China under Grant 61862007.
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Chen, Y., Ge, L., Zhang, G., Zhou, Y. (2022). Adaptive Clustering by Fast Search and Find of Density Peaks. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_65
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