Kernel-based hard clustering methods in the feature space with automatic variable weighting

DOI10.1016/j.patcog.2014.03.026zbMath1342.68277OpenAlexW2028565608MaRDI QIDQ736304

Marcelo R. P. Ferreira, Francisco de. A. T. de Carvalho

Publication date: 3 August 2016

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2014.03.026

zbMATH Keywords

feature spaceadaptive distancesclustering analysiskernel clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (1)

A distributed framework for trimmed kernel \(k\)-means clustering

Uses Software

• UCI-ml
• LIBSVM

Cites Work

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• Self-organized formation of topologically correct feature maps
• The self-organizing map
• Fuzzy \(c\)-means based clustering for linearly and nonlinearly separable data
• A survey of kernel and spectral methods for clustering
• Mathematical classification and clustering

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