Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement
As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/20/4375 |
| _version_ | 1797573974556672000 |
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| author | Jing Wang Siwu Lan Xiangyu Li Meng Lu Jingfeng Guo Chunying Zhang Bin Liu |
| author_facet | Jing Wang Siwu Lan Xiangyu Li Meng Lu Jingfeng Guo Chunying Zhang Bin Liu |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new method of hypergraph construction based on set pair theory is proposed in this paper. By means of dividing the relationship between data attributes, the set pair connection degree between samples is calculated, and the set pair distance between samples is obtained. Then, on the basis of set pair distance, the combination technique of <i>k</i>-nearest neighbor and ε radius is used to construct a hypergraph, and high-dimensional expression and hypergraph clustering are demonstrated experimentally. By performing experiments on different datasets on the Kaggle open-source dataset platform, the comparison of cluster purity, the Rand coefficient, and normalized mutual information are shown to demonstrate that this distance measurement method is more effective in high-dimensional expression and exhibits a more significant performance improvement in spectral clustering. |
| first_indexed | 2024-03-10T21:17:42Z |
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| id | doaj.art-8d04b37a81ee46d0a16d90b101a2f221 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:17:42Z |
| publishDate | 2023-10-01 |
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| series | Electronics |
| spelling | doaj.art-8d04b37a81ee46d0a16d90b101a2f2212023-11-19T16:20:50ZengMDPI AGElectronics2079-92922023-10-011220437510.3390/electronics12204375Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance MeasurementJing Wang0Siwu Lan1Xiangyu Li2Meng Lu3Jingfeng Guo4Chunying Zhang5Bin Liu6College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaBig Data and Social Computing Research Center, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaAs a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new method of hypergraph construction based on set pair theory is proposed in this paper. By means of dividing the relationship between data attributes, the set pair connection degree between samples is calculated, and the set pair distance between samples is obtained. Then, on the basis of set pair distance, the combination technique of <i>k</i>-nearest neighbor and ε radius is used to construct a hypergraph, and high-dimensional expression and hypergraph clustering are demonstrated experimentally. By performing experiments on different datasets on the Kaggle open-source dataset platform, the comparison of cluster purity, the Rand coefficient, and normalized mutual information are shown to demonstrate that this distance measurement method is more effective in high-dimensional expression and exhibits a more significant performance improvement in spectral clustering.https://www.mdpi.com/2079-9292/12/20/4375high-dimensional dataset pair distancehypergraph constructionhigh-dimensional representationhypergraph spectral clustering |
| spellingShingle | Jing Wang Siwu Lan Xiangyu Li Meng Lu Jingfeng Guo Chunying Zhang Bin Liu Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement Electronics high-dimensional data set pair distance hypergraph construction high-dimensional representation hypergraph spectral clustering |
| title | Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement |
| title_full | Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement |
| title_fullStr | Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement |
| title_full_unstemmed | Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement |
| title_short | Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement |
| title_sort | research on the method of hypergraph construction of information systems based on set pair distance measurement |
| topic | high-dimensional data set pair distance hypergraph construction high-dimensional representation hypergraph spectral clustering |
| url | https://www.mdpi.com/2079-9292/12/20/4375 |
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