An improved Bregman \(k\)-means++ algorithm via local search

DOI10.1007/978-3-030-58150-3_43zbMath1482.68221OpenAlexW3081520733MaRDI QIDQ2019501

Longkun Guo, Dan Wu, Xiaoyun Tian, Da-Chuan Xu

Publication date: 21 April 2021

Full work available at URL: https://doi.org/10.1007/978-3-030-58150-3_43

zbMATH Keywords

local search\(k\)-meansseeding algorithm\(\mu\)-similar Bregman divergences

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of information, entropy (94A17) Approximation algorithms (68W25) Computational aspects of data analysis and big data (68T09)

Cites Work

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• An iterative row-action method for interval convex programming
• Clustering for metric and nonmetric distance measures
• On the Optimality of Conditional Expectation as a Bregman Predictor
• A local search approximation algorithm for k-means clustering
• Least squares quantization in PCM
• Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering

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