Semi-supervised \(k\)-means clustering via DC programming approach

DOI10.1007/978-3-031-35305-5_4zbMath1528.90196OpenAlexW4381956187MaRDI QIDQ6134046

Tatiana V. Gruzdeva, Anton Vladimirovich Ushakov

Publication date: 21 August 2023

Published in: Mathematical Optimization Theory and Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-031-35305-5_4

zbMATH Keywords

local searchk-meansDC programmingconstrained clusteringsemi-supervised clusteringminimum-sum-of-squares clusteringcannot-linkmust-link

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26)

Cites Work

• Unnamed Item
• On local search in d.c. optimization problems
• The planar \(k\)-means problem is NP-hard
• An improved column generation algorithm for minimum sum-of-squares clustering
• Solving capacitated clustering problems
• New and efficient DCA based algorithms for minimum sum-of-squares clustering
• NP-hardness of Euclidean sum-of-squares clustering
• An effective heuristic for large-scale capacitated facility location problems
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• Optimization problems for machine learning: a survey
• K-means clustering via a nonconvex optimization approach
• A cut and branch approach for the capacitated \(p\)-median problem based on Fenchel cutting planes
• The capacitated centred clustering problem
• DILS: constrained clustering through dual iterative local search
• A matheuristic for large-scale capacitated clustering
• An exact algorithm for semi-supervised minimum sum-of-squares clustering
• On Solving Optimization Problems with Hidden Nonconvex Structures
• Constrained Clustering
• Constrained Clustering Using Column Generation
• Introduction to global optimization.