{"type":"ScholarlyArticle","title":"Mixture of Clustering Experts with Dual Consistency for Multi-View Clustering","authors":[{"name":"Daidai Zhu","slug":"daidai-zhu","given":"Daidai","family":"Zhu","affiliation":""},{"name":"Yang Zhao","slug":"yang-zhao","given":"Yang","family":"Zhao","affiliation":""},{"name":"Dandan Ma","slug":"dandan-ma","given":"Dandan","family":"Ma","affiliation":""},{"name":"Ganchao Liu","slug":"ganchao-liu","given":"Ganchao","family":"Liu","affiliation":""},{"name":"Zhiyu Jiang","slug":"zhiyu-jiang","given":"Zhiyu","family":"Jiang","affiliation":""}],"abstract":"Multi-view clustering aims to discover shared semantic structures from multiple complementary data views to improve clustering performance. However, most existing methods rely on a single clustering representation for each semantic cluster, which limits the ability to model complex cluster structures and diverse patterns in real-world data.\r\nTo address this limitation, we propose a Mixture of Clustering Experts with Dual Consistency (MoCE-DC). MoCE-DC introduces a set of learnable clustering experts that characterize the same semantic cluster from multiple perspectives within a unified prototype space. A gating mechanism is employed to adaptively select experts for each sample. MoCE-DC decomposes complex semantic clusters into multiple collaborative sub-structures, significantly enhances the ability to model intricate intra-cluster diversity.\r\nTo further ensure cross-view semantic consistency, we propose a dual-level alignment mechanism that enforces prediction consistency across views while guiding clustering assignments toward a more discriminative direction. In addition, a gating balance regularization strategy is introduced to mitigate expert collapse and promote balanced expert utilization. Extensive experiments on multiple public multi-view clustering benchmarks demonstrate that MoCE-DC outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2026/605","url":"https://ijcai.org/proceedings/2026/605","pdf":"https://ijcai.org/proceedings/2026/0605.pdf","markdown":"https://ijcai.org/proceedings/2026/605.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/605","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":"Machine Learning","track":"Main Track","number":605,"pages":{"first":5431,"last":5439},"keywords":["Machine Learning: Multi-view learning","Machine Learning: Self-supervised Learning","Machine Learning: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}