The global \(k\)-means clustering analysis based on multi-granulations nearness neighborhood

DOI10.1007/s11786-013-0150-0zbMath1262.68158OpenAlexW2003173960MaRDI QIDQ1948567

Yashuang Mu, Lidong Wang, Xiao Dong Liu

Publication date: 24 April 2013

Published in: Mathematics in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11786-013-0150-0

zbMATH Keywords

approximation spacenearnessmulti-granulationAFS theoryglobal \(k\)-means clusteringnear setsprobe functionstopology neighborhood

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Fuzzy topology (54A40) Nearness spaces (54E17)

Uses Software

• UCI-ml

Cites Work

• Nearness approximation space based on axiomatic fuzzy sets
• Modified global \(k\)-means algorithm for minimum sum-of-squares clustering problems
• Axiomatic fuzzy set theory and its applications
• The fuzzy sets and systems based on AFS. structure, EI algebra and EII algebra
• The fuzzy theory based on AFS algebras and AFS structure
• The topology of AFS structure and AFS algebras
• MGRS: a multi-granulation rough set
• Applications of Near Sets
• Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets
• Perception and Classification. A Note on Near Sets and Rough Sets
• Actor Critic Learning: A Near Set Approach
• Granular Computing: Topological and Categorical Aspects of Near and Rough Set Approaches to Granulation of Knowledge
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