The spherical \(k\)-means++ algorithm via local search

DOI10.1007/978-3-030-57602-8_12zbMath1482.68220OpenAlexW3047749157MaRDI QIDQ2039652

Xiaoyun Tian, Ling Gai, Dong-lei Du, Da-Chuan Xu

Publication date: 5 July 2021

Full work available at URL: https://doi.org/10.1007/978-3-030-57602-8_12

zbMATH Keywords

approximation algorithmlocal searchseeding algorithmcosine dissimilarityspherical \(k\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Approximation algorithms (68W25) Computational aspects of data analysis and big data (68T09)

Cites Work

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• A local search approximation algorithm for \(k\)-means clustering
• The seeding algorithms for spherical \(k\)-means clustering
• Explanation in artificial intelligence: insights from the social sciences
• Least squares quantization in PCM
• Better Guarantees for $k$-Means and Euclidean $k$-Median by Primal-Dual Algorithms
• Spherical k-Means++ Clustering
• Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
• Concept decompositions for large sparse text data using clustering
• Local search approximation algorithms for the spherical \(k\)-means problem

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