An improved density peaks clustering algorithm by automatic determination of cluster centres
The fast search and find of density peaks clustering (FDP) is an algorithm that can gain satisfactory clustering results with manual selection of the cluster centres. However, this manual selection is difficult for larger and more complex datasets, and it is easy to split a cluster into multiple sub...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.2012422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850176797281353728 |
|---|---|
| author | Hui Du Yanting Hao Zhihe Wang |
| author_facet | Hui Du Yanting Hao Zhihe Wang |
| author_sort | Hui Du |
| collection | DOAJ |
| description | The fast search and find of density peaks clustering (FDP) is an algorithm that can gain satisfactory clustering results with manual selection of the cluster centres. However, this manual selection is difficult for larger and more complex datasets, and it is easy to split a cluster into multiple subclusters. We propose an automatic determination of cluster centres algorithm (A-FDP). On the one hand, a new decision threshold is designed in A-FDP combined with the InterQuartile Range and standard deviation. We select the points larger than the decision threshold as the cluster centres. On the other hand, these cluster centres are made as nodes to construct the connected graphs. These subclusters are merged by finding the connected components of the connected graph. The results show that the A-FDP can obtain better clustering results and have higher accuracy than other classical clustering algorithms on synthetic and UCI datasets. |
| format | Article |
| id | doaj-art-bd8aa3e7e7bb465abf3648cc917f093c |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-bd8aa3e7e7bb465abf3648cc917f093c2025-08-20T02:19:11ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134185787310.1080/09540091.2021.20124222012422An improved density peaks clustering algorithm by automatic determination of cluster centresHui Du0Yanting Hao1Zhihe Wang2College of Computer Science & Engineering, Northwest Normal UniversityCollege of Computer Science & Engineering, Northwest Normal UniversityCollege of Computer Science & Engineering, Northwest Normal UniversityThe fast search and find of density peaks clustering (FDP) is an algorithm that can gain satisfactory clustering results with manual selection of the cluster centres. However, this manual selection is difficult for larger and more complex datasets, and it is easy to split a cluster into multiple subclusters. We propose an automatic determination of cluster centres algorithm (A-FDP). On the one hand, a new decision threshold is designed in A-FDP combined with the InterQuartile Range and standard deviation. We select the points larger than the decision threshold as the cluster centres. On the other hand, these cluster centres are made as nodes to construct the connected graphs. These subclusters are merged by finding the connected components of the connected graph. The results show that the A-FDP can obtain better clustering results and have higher accuracy than other classical clustering algorithms on synthetic and UCI datasets.http://dx.doi.org/10.1080/09540091.2021.2012422clusteringdensity peaksautomatic determination of cluster centressubcluster merging |
| spellingShingle | Hui Du Yanting Hao Zhihe Wang An improved density peaks clustering algorithm by automatic determination of cluster centres Connection Science clustering density peaks automatic determination of cluster centres subcluster merging |
| title | An improved density peaks clustering algorithm by automatic determination of cluster centres |
| title_full | An improved density peaks clustering algorithm by automatic determination of cluster centres |
| title_fullStr | An improved density peaks clustering algorithm by automatic determination of cluster centres |
| title_full_unstemmed | An improved density peaks clustering algorithm by automatic determination of cluster centres |
| title_short | An improved density peaks clustering algorithm by automatic determination of cluster centres |
| title_sort | improved density peaks clustering algorithm by automatic determination of cluster centres |
| topic | clustering density peaks automatic determination of cluster centres subcluster merging |
| url | http://dx.doi.org/10.1080/09540091.2021.2012422 |
| work_keys_str_mv | AT huidu animproveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres AT yantinghao animproveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres AT zhihewang animproveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres AT huidu improveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres AT yantinghao improveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres AT zhihewang improveddensitypeaksclusteringalgorithmbyautomaticdeterminationofclustercentres |