A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory
The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clus...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8918459/ |
| _version_ | 1830130554949861376 |
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| author | Limin Wang Wei Zhou Honghuan Wang Milan Parmar Xuming Han |
| author_facet | Limin Wang Wei Zhou Honghuan Wang Milan Parmar Xuming Han |
| author_sort | Limin Wang |
| collection | DOAJ |
| description | The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results. |
| first_indexed | 2024-12-17T05:57:57Z |
| format | Article |
| id | doaj.art-df7959a685904d23a14db762cf230093 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:57:57Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-df7959a685904d23a14db762cf2300932022-12-21T22:00:57ZengIEEEIEEE Access2169-35362019-01-01717438017439010.1109/ACCESS.2019.29572428918459A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor TheoryLimin Wang0https://orcid.org/0000-0002-0576-3950Wei Zhou1https://orcid.org/0000-0003-2227-9084Honghuan Wang2https://orcid.org/0000-0003-0276-9537Milan Parmar3https://orcid.org/0000-0002-7596-407XXuming Han4https://orcid.org/0000-0002-6213-5600School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun, ChinaThe density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results.https://ieeexplore.ieee.org/document/8918459/Density peaks clusteringhalo nodeK-nearest neighborlow-density nodes |
| spellingShingle | Limin Wang Wei Zhou Honghuan Wang Milan Parmar Xuming Han A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory IEEE Access Density peaks clustering halo node K-nearest neighbor low-density nodes |
| title | A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory |
| title_full | A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory |
| title_fullStr | A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory |
| title_full_unstemmed | A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory |
| title_short | A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory |
| title_sort | novel density peaks clustering halo node assignment method based on k nearest neighbor theory |
| topic | Density peaks clustering halo node K-nearest neighbor low-density nodes |
| url | https://ieeexplore.ieee.org/document/8918459/ |
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