Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm
Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distanc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/1/3 |
| _version_ | 1797344543501189120 |
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| author | Huajuan Huang Hao Wu Xiuxi Wei Yongquan Zhou |
| author_facet | Huajuan Huang Hao Wu Xiuxi Wei Yongquan Zhou |
| author_sort | Huajuan Huang |
| collection | DOAJ |
| description | Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (<i>d<sub>c</sub></i>). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of <i>d<sub>c</sub></i> when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize <i>d<sub>c</sub></i> for maximizing accuracy, achieving automatic determination of <i>d<sub>c</sub></i>. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC. |
| first_indexed | 2024-03-08T11:04:15Z |
| format | Article |
| id | doaj.art-4f1acc30623947fa8adfcd0ee4d50d02 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T11:04:15Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-4f1acc30623947fa8adfcd0ee4d50d022024-01-26T15:15:17ZengMDPI AGBiomimetics2313-76732023-12-0191310.3390/biomimetics9010003Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow AlgorithmHuajuan Huang0Hao Wu1Xiuxi Wei2Yongquan Zhou3College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, ChinaClustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (<i>d<sub>c</sub></i>). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of <i>d<sub>c</sub></i> when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize <i>d<sub>c</sub></i> for maximizing accuracy, achieving automatic determination of <i>d<sub>c</sub></i>. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.https://www.mdpi.com/2313-7673/9/1/3clusteringdensity peak clusteringcutoff distanceblack widow algorithm |
| spellingShingle | Huajuan Huang Hao Wu Xiuxi Wei Yongquan Zhou Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm Biomimetics clustering density peak clustering cutoff distance black widow algorithm |
| title | Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm |
| title_full | Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm |
| title_fullStr | Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm |
| title_full_unstemmed | Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm |
| title_short | Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm |
| title_sort | optimization of density peak clustering algorithm based on improved black widow algorithm |
| topic | clustering density peak clustering cutoff distance black widow algorithm |
| url | https://www.mdpi.com/2313-7673/9/1/3 |
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