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Therefore, how to find the subspaces that better represent the original data becomes a research challenge. However, most of the reported methods are based on the premise that the contributions of different features are equal, which may not be ideal for real scenarios, i.e., the contributions of the important features may be overwhelmed by a large amount of redundant features. In this study, a weighted subspace fuzzy clustering (WSFC) model with a locality preservation mechanism is presented, which can adaptively capture the importance of different features, achieve an optimal lower-dimensional subspace, and perform fuzzy clustering simultaneously. Since each feature can be well quantified in terms of its importance, the proposed model exhibits the sparsity and robustness of fuzzy clustering. The intrinsic geometrical structures of data can also be preserved while enhancing the interpretability of clustering tasks. Extensive experimental results show that WSFC can allocate appropriate weights to different features according to data distributions and clustering tasks and achieve superior performance compared to other clustering models on real-world datasets.<\/jats:p>","DOI":"10.1155\/2024\/6696775","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T18:50:06Z","timestamp":1706727006000},"page":"1-18","source":"Crossref","is-referenced-by-count":1,"title":["Weighted Subspace Fuzzy Clustering with Adaptive Projection"],"prefix":"10.1155","volume":"2024","author":[{"given":"Jie","family":"Zhou","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China"},{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China"},{"name":"SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chucheng","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China"},{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4519-5089","authenticated-orcid":true,"given":"Can","family":"Gao","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China"},{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China"},{"name":"SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangbo","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinrui","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Statistics, Shenzhen University, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22668"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22339"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1002\/int.22409"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1002\/int.22319"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2020.2964552"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03698-8"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008694"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1002\/int.21915"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108584"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1002\/int.22901"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108550"},{"key":"12","first-page":"849","article-title":"On spectral clustering: analysis and an algorithm","volume-title":"Advances in Neural Information Processing Systems","author":"A. 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