{"type":"ScholarlyArticle","title":"Neural Capacitated Clustering","authors":[{"name":"Jonas K. Falkner","slug":"jonas-k-falkner","given":"Jonas K.","family":"Falkner","affiliation":"Institute of Computer Science , University of Hildesheim , Hildesheim , Germany"},{"name":"Lars Schmidt-Thieme","slug":"lars-schmidt-thieme","given":"Lars","family":"Schmidt-Thieme","affiliation":"Institute of Computer Science , University of Hildesheim , Hildesheim , Germany"}],"abstract":"Recent work on deep clustering has found new promising methods also for constrained clustering problems. \r\nTheir typically pairwise constraints often can be used to guide the partitioning of the data.\r\nMany problems however, feature cluster-level constraints, e.g. the Capacitated Clustering Problem (CCP), where each point has a weight and the total weight sum of all points in each cluster is bounded by a prescribed capacity. \r\nIn this paper we propose a new method for the CCP, Neural Capacited Clustering, that learns a neural network to predict the assignment probabilities of points to cluster centers from a data set of optimal or near optimal past solutions of other problem instances. \r\nDuring inference, the resulting scores are then used in an iterative k-means like procedure to refine the assignment under capacity constraints. \r\nIn our experiments on artificial data and two real world datasets our approach outperforms several state-of-the-art mathematical and heuristic solvers from the literature. \r\nMoreover, we apply our method in the context of a cluster-first-route-second approach to the Capacitated Vehicle Routing Problem (CVRP) and show competitive results on the well-known Uchoa benchmark.","doi":"10.24963/ijcai.2023/410","url":"https://ijcai.org/proceedings/2023/410","pdf":"https://ijcai.org/proceedings/2023/0410.pdf","markdown":"https://ijcai.org/proceedings/2023/410.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/410","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":410,"pages":{"first":3686,"last":3694},"keywords":["Machine Learning: ML: Geometric learning","Constraint Satisfaction and Optimization: CSO: Applications","Machine Learning: ML: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Arthur and Vassilvitskii, 2006] David Arthur and Sergei Vassilvitskii. k-means++: The advantages of careful seed- ing. 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