A fast and recursive algorithm for clustering large datasets with \(k\)-medians
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Publication:434902
DOI10.1016/j.csda.2011.11.019zbMath1243.62087arXiv1101.4179OpenAlexW2003188728MaRDI QIDQ434902
Jean-Marie Monnez, Peggy Cénac, Hervé Cardot
Publication date: 16 July 2012
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1101.4179
stochastic approximationrecursive estimatorsaveraginghigh dimensional datastochastic gradient\(k\)-medoidsonline clusteringpartitioning around medoidsRobbins-Monro
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Stochastic approximation (62L20) Sequential estimation (62L12)
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Cites Work
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- Online wavelet-based density estimation for non-stationary streaming data
- A general trimming approach to robust cluster analysis
- Editorial: Machine learning and robust data mining
- \(L_1\)-quantization and clustering in Banach spaces
- A review of robust clustering methods
- Almost sure convergence of stochastic gradient processes with matrix step sizes
- On a Geometric Notion of Quantiles for Multivariate Data
- Large-Scale Machine Learning with Stochastic Gradient Descent
- Stochastic Approximation for Multivariate and Functional Median
- Hybrid hierarchical clustering with applications to microarray data
- Finding Groups in Data
- Acceleration of Stochastic Approximation by Averaging
- Printer graphics for clustering
- Asymptotic behaviour of classification maximum likelihood estimates
- Asymptotic Almost Sure Efficiency of Averaged Stochastic Algorithms
- Robustness Properties of k Means and Trimmed k Means
- Data Clustering: Theory, Algorithms, and Applications