An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood
Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (dc), the cluster number (k) and the selec...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005622000704 |
| _version_ | 1797998997102657536 |
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| author | Wendi Zuo Xinmin Hou |
| author_facet | Wendi Zuo Xinmin Hou |
| author_sort | Wendi Zuo |
| collection | DOAJ |
| description | Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (dc), the cluster number (k) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN. |
| first_indexed | 2024-04-11T10:57:43Z |
| format | Article |
| id | doaj.art-97a70b27320349dcac4df70376affb78 |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-04-11T10:57:43Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-97a70b27320349dcac4df70376affb782022-12-22T04:28:43ZengElsevierArray2590-00562022-09-0115100232An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhoodWendi Zuo0Xinmin Hou1School of Data Science, University of Science and Technology of China, Hefei, Anhui 230026, ChinaSchool of Data Science, University of Science and Technology of China, Hefei, Anhui 230026, China; School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China; CAS Key Laboratory of Wu Wen-Tsun Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China; Corresponding author at: School of Data Science, University of Science and Technology of China, Hefei, Anhui 230026, China.Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (dc), the cluster number (k) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.http://www.sciencedirect.com/science/article/pii/S2590005622000704ClusteringDensity peaksNatural nearest neighborhoodProbability propagation |
| spellingShingle | Wendi Zuo Xinmin Hou An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood Array Clustering Density peaks Natural nearest neighborhood Probability propagation |
| title | An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| title_full | An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| title_fullStr | An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| title_full_unstemmed | An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| title_short | An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| title_sort | improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood |
| topic | Clustering Density peaks Natural nearest neighborhood Probability propagation |
| url | http://www.sciencedirect.com/science/article/pii/S2590005622000704 |
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