Clustering explanation based on multi-hyperrectangle
Abstract Clustering plays a crucial role in data mining and pattern recognition, but the interpretation of clustering results is often challenging. Existing interpretation methods usually lack an intuitive and accurate description of irregular shapes and high dimensional datas. This paper proposes a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-81141-3 |
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| _version_ | 1846137103847522304 |
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| author | Tao Zeng Caiming Zhong Tiejun Pan |
| author_facet | Tao Zeng Caiming Zhong Tiejun Pan |
| author_sort | Tao Zeng |
| collection | DOAJ |
| description | Abstract Clustering plays a crucial role in data mining and pattern recognition, but the interpretation of clustering results is often challenging. Existing interpretation methods usually lack an intuitive and accurate description of irregular shapes and high dimensional datas. This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results. MHR first generates initial hyperrectangles to cover each cluster, and then these hyper-rectangles are gradually merged until the optimal shape is obtained to fit the cluster. The advantage of this method is that it recognizes the shape of irregular clusters and finds the optimal number of hyper-rectangles based on the hierarchical tree structure, which discovers structural relationships between rectangles. Furthermore, we propose a refinement method to improve the tightness of the hyperrectangles, resulting in more precise and comprehensible explanations. Experimental results demonstrate that MHR significantly outperforms existing methods in both the tightness and accuracy of cluster interpretation, highlighting its effectiveness and innovation in addressing the challenges of clustering interpretation. |
| format | Article |
| id | doaj-art-09d2c237080b4e4b80549427e4ef22f3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-09d2c237080b4e4b80549427e4ef22f32024-12-08T12:27:28ZengNature PortfolioScientific Reports2045-23222024-12-0114111810.1038/s41598-024-81141-3Clustering explanation based on multi-hyperrectangleTao Zeng0Caiming Zhong1Tiejun Pan2College of Science and Technology, Ningbo UniversityCollege of Science and Technology, Ningbo UniversityCollege of Science and Technology, Ningbo UniversityAbstract Clustering plays a crucial role in data mining and pattern recognition, but the interpretation of clustering results is often challenging. Existing interpretation methods usually lack an intuitive and accurate description of irregular shapes and high dimensional datas. This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results. MHR first generates initial hyperrectangles to cover each cluster, and then these hyper-rectangles are gradually merged until the optimal shape is obtained to fit the cluster. The advantage of this method is that it recognizes the shape of irregular clusters and finds the optimal number of hyper-rectangles based on the hierarchical tree structure, which discovers structural relationships between rectangles. Furthermore, we propose a refinement method to improve the tightness of the hyperrectangles, resulting in more precise and comprehensible explanations. Experimental results demonstrate that MHR significantly outperforms existing methods in both the tightness and accuracy of cluster interpretation, highlighting its effectiveness and innovation in addressing the challenges of clustering interpretation.https://doi.org/10.1038/s41598-024-81141-3 |
| spellingShingle | Tao Zeng Caiming Zhong Tiejun Pan Clustering explanation based on multi-hyperrectangle Scientific Reports |
| title | Clustering explanation based on multi-hyperrectangle |
| title_full | Clustering explanation based on multi-hyperrectangle |
| title_fullStr | Clustering explanation based on multi-hyperrectangle |
| title_full_unstemmed | Clustering explanation based on multi-hyperrectangle |
| title_short | Clustering explanation based on multi-hyperrectangle |
| title_sort | clustering explanation based on multi hyperrectangle |
| url | https://doi.org/10.1038/s41598-024-81141-3 |
| work_keys_str_mv | AT taozeng clusteringexplanationbasedonmultihyperrectangle AT caimingzhong clusteringexplanationbasedonmultihyperrectangle AT tiejunpan clusteringexplanationbasedonmultihyperrectangle |