{"type":"ScholarlyArticle","title":"Resisting Label Drift: Real-Time Multi-View Clustering with Semantic Consistency","authors":[{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":""},{"name":"Suyuan Liu","slug":"suyuan-liu","given":"Suyuan","family":"Liu","affiliation":""},{"name":"Hao Tan","slug":"hao-tan","given":"Hao","family":"Tan","affiliation":""},{"name":"Yangfan Du","slug":"yangfan-du","given":"Yangfan","family":"Du","affiliation":""},{"name":"Bowen Zhang","slug":"bowen-zhang","given":"Bowen","family":"Zhang","affiliation":""},{"name":"Wenpeng Lu","slug":"wenpeng-lu","given":"Wenpeng","family":"Lu","affiliation":""},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":""}],"abstract":"Real-time clustering of dynamic multi-view data streams is a critical yet challenging task in open-world applications. While several methods have been proposed to address this task, most of them extract features incrementally but fail to output instant clustering results for the current batch. In addition, they neglect semantic consistency, causing the cluster labels of identical concepts to drift unpredictably due to independent processing. To address these limitations, we propose a real-time semantic consistent incremental multi-view clustering framework. Specifically, we constructs a compact historical knowledge base via an adaptive diversity-aware selection mechanism, which guides the clustering of incoming data, enabling immediate inference without accessing the full history. Furthermore, we introduce a semantic alignment strategy based on consensus centers to ensure robust label consistency over time. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.","doi":"10.24963/ijcai.2026/319","url":"https://ijcai.org/proceedings/2026/319","pdf":"https://ijcai.org/proceedings/2026/0319.pdf","markdown":"https://ijcai.org/proceedings/2026/319.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/319","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":319,"pages":{"first":2870,"last":2878},"keywords":["Machine Learning: Clustering","Data Mining: Mining data streams","Machine Learning: Multi-view learning","Machine Learning: Online learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}