A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process
Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Sp...
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| Vydáno v: | Physica A Ročník 523; s. 702 - 713 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.06.2019
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Spiral. The research results indicate that assignment process appears to be the most significant step in deciding the success of the clustering performance. Therefore, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which aims to overcome the weakness of DPC. The proposed DPC-KNN integrates the idea of k nearest neighbors into the distance computation and assignment process, which is more reasonable. It can be seen from experimental results that the DPC-KNN algorithm is more feasible and effective, compared with K-means, DBSCAN and DPC.
•K nearest neighbors is adopted to solve domino effect problem in density peaks clustering.•The capability of aggregating some non-spherical clusters is enhanced effectively.•Experimental results show that the DPC-KNN algorithm is more effective. |
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| AbstractList | Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Spiral. The research results indicate that assignment process appears to be the most significant step in deciding the success of the clustering performance. Therefore, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which aims to overcome the weakness of DPC. The proposed DPC-KNN integrates the idea of k nearest neighbors into the distance computation and assignment process, which is more reasonable. It can be seen from experimental results that the DPC-KNN algorithm is more feasible and effective, compared with K-means, DBSCAN and DPC.
•K nearest neighbors is adopted to solve domino effect problem in density peaks clustering.•The capability of aggregating some non-spherical clusters is enhanced effectively.•Experimental results show that the DPC-KNN algorithm is more effective. |
| Author | Jiang, Jianhua Li, Keqin Chen, Yujun Wang, Limin Meng, Xianqiu |
| Author_xml | – sequence: 1 givenname: Jianhua surname: Jiang fullname: Jiang, Jianhua email: jjh@jlufe.edu.cn organization: Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, PR China – sequence: 2 givenname: Yujun surname: Chen fullname: Chen, Yujun organization: Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, PR China – sequence: 3 givenname: Xianqiu surname: Meng fullname: Meng, Xianqiu organization: Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, PR China – sequence: 4 givenname: Limin surname: Wang fullname: Wang, Limin organization: Department of Data Science, Jilin University of Finance and Economics, Changchun 130117, PR China – sequence: 5 givenname: Keqin surname: Li fullname: Li, Keqin email: lik@newpaltz.edu organization: Department of Computer Science, State University of New York, New Paltz, NY 12561, USA |
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| Snippet | Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has... |
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| Title | A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process |
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