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- W4320496029 abstract "Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean distance is prone to misclassification. When dealing with clusters of non-uniform density, it is very difficult to identify true clustering centers in the decision graph; (ii) the clustering centers need to be manually selected; (iii) the chain reaction; an incorrectly assigned point will affect the clustering outcome. To settle the above limitations, we propose an improved density peaks clustering algorithm based on a divergence distance and tissue—like P system (TP-DSDPC in short). In the proposed algorithm, a novel distance measure is introduced to accurately estimate the local density and relative distance of each point. Then, clustering centers are automatically selected by the score value. A tissue—like P system carries out the entire algorithm process. In terms of the three evaluation metrics, the improved algorithm outperforms the other comparison algorithms using multiple synthetic and real-world datasets." @default.
- W4320496029 created "2023-02-14" @default.
- W4320496029 creator A5040643591 @default.
- W4320496029 creator A5046658130 @default.
- W4320496029 date "2023-02-10" @default.
- W4320496029 modified "2023-09-26" @default.
- W4320496029 title "Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System" @default.
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- W4320496029 doi "https://doi.org/10.3390/app13042293" @default.
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