{"type":"ScholarlyArticle","title":"COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints","authors":[{"name":"Toon Van Craenendonck","slug":"toon-van-craenendonck","given":"Toon","family":"Van Craenendonck","affiliation":"Department of Computer Science , KU Leuven , Belgium"},{"name":"Sebastijan Dumancic","slug":"sebastijan-dumancic","given":"Sebastijan","family":"Dumancic","affiliation":"Department of Computer Science , KU Leuven , Belgium"},{"name":"Hendrik Blockeel","slug":"hendrik-blockeel","given":"Hendrik","family":"Blockeel","affiliation":"Department of Computer Science , KU Leuven , Belgium"}],"abstract":"Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms select the most useful constraints to query, aiming to produce a good clustering using as few constraints as possible. We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints. In its merging step, COBRA is able to keep the number of pairwise queries low by maximally exploiting constraint transitivity and entailment. We experimentally show that COBRA outperforms the state of the art in terms of clustering quality and runtime, without requiring the number of clusters in advance.","doi":"10.24963/ijcai.2017/400","url":"https://ijcai.org/proceedings/2017/400","pdf":"https://ijcai.org/proceedings/2017/0400.pdf","markdown":"https://ijcai.org/proceedings/2017/400.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/400","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":400,"pages":{"first":2871,"last":2877},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Active Learning","Machine Learning: Semi-Supervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bilenko et al., 2004] Mikhail Bilenko, Sugato Basu, and Raymond J. 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