ConDPC: Data Connectivity-Based Density Peak Clustering
As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is sim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12812 |
| _version_ | 1827641953156595712 |
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| author | Yujuan Zou Zhijian Wang |
| author_facet | Yujuan Zou Zhijian Wang |
| author_sort | Yujuan Zou |
| collection | DOAJ |
| description | As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC. |
| first_indexed | 2024-03-09T17:21:52Z |
| format | Article |
| id | doaj.art-354ca8b430ac4789b76c3e5c22b6d89a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:21:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-354ca8b430ac4789b76c3e5c22b6d89a2023-11-24T13:05:11ZengMDPI AGApplied Sciences2076-34172022-12-0112241281210.3390/app122412812ConDPC: Data Connectivity-Based Density Peak ClusteringYujuan Zou0Zhijian Wang1College of Computer and Information, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information, Hohai University, Nanjing 211100, ChinaAs a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC.https://www.mdpi.com/2076-3417/12/24/12812clusteringconnectivityEuclidean distanceneighbor distancedensity difference |
| spellingShingle | Yujuan Zou Zhijian Wang ConDPC: Data Connectivity-Based Density Peak Clustering Applied Sciences clustering connectivity Euclidean distance neighbor distance density difference |
| title | ConDPC: Data Connectivity-Based Density Peak Clustering |
| title_full | ConDPC: Data Connectivity-Based Density Peak Clustering |
| title_fullStr | ConDPC: Data Connectivity-Based Density Peak Clustering |
| title_full_unstemmed | ConDPC: Data Connectivity-Based Density Peak Clustering |
| title_short | ConDPC: Data Connectivity-Based Density Peak Clustering |
| title_sort | condpc data connectivity based density peak clustering |
| topic | clustering connectivity Euclidean distance neighbor distance density difference |
| url | https://www.mdpi.com/2076-3417/12/24/12812 |
| work_keys_str_mv | AT yujuanzou condpcdataconnectivitybaseddensitypeakclustering AT zhijianwang condpcdataconnectivitybaseddensitypeakclustering |