Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering

DOI10.1007/s10898-010-9571-3zbMath1213.90205OpenAlexW2100485099MaRDI QIDQ628745

Pierre Hansen, Daniel Aloise

Publication date: 14 March 2011

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-010-9571-3

zbMATH Keywords

computational results\(k\)-means

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Combinatorial optimization (90C27) Boolean programming (90C09)

Related Items

A generic approach to proving NP-hardness of partition type problems, New heuristic for harmonic means clustering, SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering, Mixed-integer programming techniques for the minimum sum-of-squares clustering problem, An improved column generation algorithm for minimum sum-of-squares clustering, Column generation bounds for numerical microaggregation, The Complexity Status of Problems Related to Sparsest Cuts, Branch-and-cut approaches for \(p\)-cluster editing, Pseudopolynomial algorithms for certain computationally hard vector subset and cluster analysis problems

Uses Software

• J-MEANS
• clusfind

Cites Work

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