Improved roughk-means clustering algorithm based on weighted distance measure with Gaussian function

DOI10.1080/00207160.2015.1124099zbMath1362.62133OpenAlexW2397015893MaRDI QIDQ5737888

Teng-Fei Zhang, Fumin Ma

Publication date: 30 May 2017

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207160.2015.1124099

zbMATH Keywords

clustering algorithmrough set theoryweighted distance measureGaussian functionrough \(k\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Knowledge representation (68T30)

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Cites Work

• Rough sets: some extensions
• Shadowed \(c\)-means: integrating fuzzy and rough clustering
• An initialization method for the \(K\)-means algorithm using neighborhood model
• Semi-supervised outlier detection based on fuzzy rough \(c\)-means clustering
• Interval set clustering of web users with rough \(K\)-means
• An extension to rough \(c\)-means clustering based on decision-theoretic rough sets model
• Some refinements of rough \(k\)-means clustering
• A Partitive Rough Clustering Algorithm
• An Extension to Rough c-Means Clustering Algorithm Based on Boundary Area Elements Discrimination

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