Clustering by Constructing Hyper-Planes
As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9426895/ |
| _version_ | 1811273970305990656 |
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| author | Luhong Diao Manman Deng Jinying Gao |
| author_facet | Luhong Diao Manman Deng Jinying Gao |
| author_sort | Luhong Diao |
| collection | DOAJ |
| description | As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters’ number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods’ best results which are selected manually. |
| first_indexed | 2024-04-12T23:09:15Z |
| format | Article |
| id | doaj.art-40127b70ed2447c89b05500433e651fc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:09:15Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-40127b70ed2447c89b05500433e651fc2022-12-22T03:12:50ZengIEEEIEEE Access2169-35362021-01-019701677018110.1109/ACCESS.2021.30785849426895Clustering by Constructing Hyper-PlanesLuhong Diao0https://orcid.org/0000-0002-2882-2490Manman Deng1Jinying Gao2Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, ChinaBeijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, ChinaBeijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, ChinaAs a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters’ number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods’ best results which are selected manually.https://ieeexplore.ieee.org/document/9426895/Clustering algorithmhyper-planessupport vector machinevalidity index |
| spellingShingle | Luhong Diao Manman Deng Jinying Gao Clustering by Constructing Hyper-Planes IEEE Access Clustering algorithm hyper-planes support vector machine validity index |
| title | Clustering by Constructing Hyper-Planes |
| title_full | Clustering by Constructing Hyper-Planes |
| title_fullStr | Clustering by Constructing Hyper-Planes |
| title_full_unstemmed | Clustering by Constructing Hyper-Planes |
| title_short | Clustering by Constructing Hyper-Planes |
| title_sort | clustering by constructing hyper planes |
| topic | Clustering algorithm hyper-planes support vector machine validity index |
| url | https://ieeexplore.ieee.org/document/9426895/ |
| work_keys_str_mv | AT luhongdiao clusteringbyconstructinghyperplanes AT manmandeng clusteringbyconstructinghyperplanes AT jinyinggao clusteringbyconstructinghyperplanes |