Interpretation and optimization of the \(k\)-means algorithm.

DOI10.1007/s10492-014-0063-5zbMath1340.68112OpenAlexW1992718985MaRDI QIDQ464720

Rudolf Scitovski, Kristian Sabo

Publication date: 29 October 2014

Published in: Applications of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10492-014-0063-5

zbMATH Keywords

clusteringVoronoi diagram\(k\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Related Items (2)

The adaptation of the \(k\)-means algorithm to solving the multiple ellipses detection problem by using an initial approximation obtained by the DIRECT global optimization algorithm. ⋮ A combination of \(k\)-means and \texttt{DBSCAN} algorithm for solving the multiple generalized circle detection problem

Uses Software

• LGO
• DIRECT
• flexclust
• SymDIRECT

Cites Work

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• A modification of the \texttt{DIRECT} method for Lipschitz global optimization for a symmetric function
• Fast modified global \(k\)-means algorithm for incremental cluster construction
• A review of recent advances in global optimization
• Modified global \(k\)-means algorithm for minimum sum-of-squares clustering problems
• A generalized Weiszfeld method for the multi-facility location problem
• A toolbox for \(K\)-centroids cluster analysis
• Lipschitzian optimization without the Lipschitz constant
• Global optimization in action. Continuous and Lipschitz optimization: algorithms, implementations and applications
• One-dimensional center-based l 1-clustering method
• On fuzzy cluster validity indices
• Building initial partitions through sampling techniques
• Firefly Algorithms for Multimodal Optimization
• Data Clustering: Theory, Algorithms, and Applications

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