Exact algorithms for size constrained 2-clustering in the plane

DOI10.1016/j.tcs.2015.10.005zbMath1339.68226DBLPjournals/tcs/LinBG16OpenAlexW2132011326WikidataQ59538789 ScholiaQ59538789MaRDI QIDQ288798

Massimiliano Goldwurm, Jianyi Lin, Alberto Bertoni

Publication date: 27 May 2016

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2015.10.005

zbMATH Keywords

convex hullEuclidean norm\(k\)-setalgorithms for clusteringcluster size constraintsManhattan norm

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

NP-hardness of \(m\)-dimensional weighted matching problems ⋮ On the complexity of clustering with relaxed size constraints in fixed dimension ⋮ On the Complexity of Clustering with Relaxed Size Constraints

Uses Software

• ElemStatLearn
• PRMLT

Cites Work

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• The planar \(k\)-means problem is NP-hard
• \(k\)-means requires exponentially many iterations even in the plane
• NP-hardness of Euclidean sum-of-squares clustering
• Norm statistics and the complexity of clustering problems
• Maintenance of configurations in the plane
• Improved bounds for planar \(k\)-sets and related problems
• Minimum sum of squares clustering in a low dimensional space
• Exact Algorithms for 2-Clustering with Size Constraints in the Euclidean Plane
• Integer Programming and the Theory of Grouping

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