Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System
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 w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2293 |
| _version_ | 1797622545742036992 |
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| author | Fuhua Ge Xiyu Liu |
| author_facet | Fuhua Ge Xiyu Liu |
| author_sort | Fuhua Ge |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:12:50Z |
| format | Article |
| id | doaj.art-3c7508aeb0e14b3e9fbd85818626942f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:12:50Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3c7508aeb0e14b3e9fbd85818626942f2023-11-16T18:53:40ZengMDPI AGApplied Sciences2076-34172023-02-01134229310.3390/app13042293Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P SystemFuhua Ge0Xiyu Liu1Academy of Management Science, Business School, Shandong Normal University, Jinan 250014, ChinaAcademy of Management Science, Business School, Shandong Normal University, Jinan 250014, ChinaDensity 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.https://www.mdpi.com/2076-3417/13/4/2293density peaks clusteringdivergence distanceclustering centers selectiontissue—like P system |
| spellingShingle | Fuhua Ge Xiyu Liu Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System Applied Sciences density peaks clustering divergence distance clustering centers selection tissue—like P system |
| title | Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System |
| title_full | Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System |
| title_fullStr | Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System |
| title_full_unstemmed | Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System |
| title_short | Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System |
| title_sort | density peaks clustering algorithm based on a divergence distance and tissue like p system |
| topic | density peaks clustering divergence distance clustering centers selection tissue—like P system |
| url | https://www.mdpi.com/2076-3417/13/4/2293 |
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