Fast modified global \(k\)-means algorithm for incremental cluster construction
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Publication:621088
DOI10.1016/j.patcog.2010.10.018zbMath1213.68514OpenAlexW1979607399MaRDI QIDQ621088
Dean Webb, Adil M. Bagirov, Julien Ugon
Publication date: 2 February 2011
Published in: Pattern Recognition (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.patcog.2010.10.018
nonsmooth optimization\(k\)-means algorithmminimum sum-of-squares clusteringglobal \(k\)-means algorithm
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Cites Work
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- Analysis of global \(k\)-means, an incremental heuristic for minimum sum-of-squares clustering
- Modified global \(k\)-means algorithm for minimum sum-of-squares clustering problems
- A comparison of two dual-based procedures for solving the p-median problem
- Unsupervised and supervised data classification via nonsmooth and global optimization (with comments and rejoinder)
- Variable neighborhood decomposition search
- A global optimization approach to classification
- Batch and median neural gas
- A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems
- The hyperbolic smoothing clustering method
- Better streaming algorithms for clustering problems
- Well-Separated Clusters and Optimal Fuzzy Partitions
- A Branch and Bound Clustering Algorithm
- Cluster analysis by simulated annealing
- An Interior Point Algorithm for Minimum Sum-of-Squares Clustering
- Evaluation of a Branch and Bound Algorithm for Clustering
- J-MEANS: A new local search heuristic for minimum sum of squares clustering
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