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In: Proceedings of the SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp. 270\u2013279.","DOI":"10.1145\/1869790.1869829"},{"issue":"2","key":"10.1016\/j.engappai.2026.114676_b70","doi-asserted-by":"crossref","first-page":"83","DOI":"10.26599\/BDMA.2018.9020003","article-title":"Multi-view clustering: A survey","volume":"1","author":"Yang","year":"2018","journal-title":"Big Data Min. Anal."},{"key":"10.1016\/j.engappai.2026.114676_b71","unstructured":"Yu, S., Dong, Z., Wang, S., Wan, X., Liu, Y., Liang, W., Zhang, P., Tu, W., Liu, X., 2024. Towards resource-friendly, extensible and stable incomplete multi-view clustering. 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In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2577\u20132585.","DOI":"10.1109\/CVPR.2019.00268"},{"issue":"7","key":"10.1016\/j.engappai.2026.114676_b78","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TPAMI.2018.2847335","article-title":"Binary multi-view clustering","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114676_b79","unstructured":"Zhao, H., Liu, H., Fu, Y., 2016. Incomplete multi-modal visual data grouping.. In: Proceedings of the International Joint Conference on Artificial Intelligence. pp. 2392\u20132398."},{"issue":"11","key":"10.1016\/j.engappai.2026.114676_b80","doi-asserted-by":"crossref","first-page":"10842","DOI":"10.1109\/TII.2023.3241587","article-title":"Trustworthy fault diagnosis with uncertainty estimation through evidential convolutional neural networks","volume":"19","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Ind. 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