{"type":"ScholarlyArticle","title":"Self-paced Consensus Clustering with Bipartite Graph","authors":[{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":"School of Computer Science and Technology , Anhui University; State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences"},{"name":"Liang Du","slug":"liang-du","given":"Liang","family":"Du","affiliation":"School of Computer and Information Technology , Shanxi University"},{"name":"Xuejun Li","slug":"xuejun-li","given":"Xuejun","family":"Li","affiliation":"School of Computer Science and Technology , Anhui University"}],"abstract":"Consensus clustering provides a framework to ensemble multiple clustering results to obtain a consensus and robust  result. Most existing consensus clustering methods usually apply all data to ensemble learning, whereas ignoring the side effects caused by some difficult or unreliable instances. To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning. We first construct an initial bipartite graph from the multiple base clustering results, where the nodes represent the instances and clusters and the edges indicate that an instance belongs to a cluster. Then, we learn a structured bipartite graph from the initial one by self-paced learning, i.e., we automatically decide the reliability of each edge and involves the edges into graph learning in order of their reliability. At last, we obtain the final consensus clustering result from the learned bipartite graph. The extensive experimental results demonstrate the effectiveness and superiority of the proposed method.","doi":"10.24963/ijcai.2020/295","url":"https://ijcai.org/proceedings/2020/295","pdf":"https://ijcai.org/proceedings/2020/0295.pdf","markdown":"https://ijcai.org/proceedings/2020/295.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/295","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":295,"pages":{"first":2133,"last":2139},"keywords":["Machine Learning: Ensemble Methods","Machine Learning: Clustering"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24587/file_24587.m3u8","length_sec":287,"provider":"hls"}],"references":[{"position":1,"raw":"[Azimi and Fern, 2009] Javad Azimi and Xiaoli Fern. 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