\(K\)-harmonic means data clustering with simulated annealing heuristic

DOI10.1016/j.amc.2006.05.166zbMath1114.65009OpenAlexW2072545917MaRDI QIDQ879469

Zülal Güngör, Alper Ünler

Publication date: 14 May 2007

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2006.05.166

zbMATH Keywords

algorithmnumerical examplesclustering\(K\)-meansfuzzy \(K\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Uses Software

• J-MEANS
• UCI-ml
• Tabu search

Cites Work

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• Optimization by Simulated Annealing
• Randomised local search algorithm for the clustering problem
• Initialization insensitive LVQ algorithm based on cost-function adaptation
• The complexity of the generalized Lloyd - Max problem (Corresp.)
• Stochastic K-means algorithm for vector quantization
• Equation of State Calculations by Fast Computing Machines
• Letter to the Editor—A Monte Carlo Method for the Approximate Solution of Certain Types of Constrained Optimization Problems
• J-MEANS: A new local search heuristic for minimum sum of squares clustering
• Vector quantization based on genetic simulated annealing
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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