A Unified Framework for Clustering Constrained Data without Locality Property
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Publication:5363012
DOI10.1137/1.9781611973730.97zbMath1371.68291OpenAlexW2596841574MaRDI QIDQ5363012
Publication date: 5 October 2017
Published in: Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/1.9781611973730.97
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)
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Improved PTAS for the constrained \(k\)-means problem ⋮ Unnamed Item ⋮ Faster algorithms for the constrained \(k\)-means problem ⋮ On Geometric Prototype and Applications ⋮ A unified framework of FPT approximation algorithms for clustering problems ⋮ FPT Approximation for Constrained Metric k-Median/Means ⋮ Faster balanced clusterings in high dimension ⋮ Some Estimates on the Discretization of Geometric Center-Based Problems in High Dimensions ⋮ A unified framework for clustering constrained data without locality property ⋮ Learning the truth vector in high dimensions ⋮ An approximation algorithm for the uniform capacitated \(k\)-means problem
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