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

DOI10.1016/j.patcog.2015.02.020zbMath1394.68315OpenAlexW2057309387MaRDI QIDQ1669597

Ioannis Pitas, Nikolaos Nikolaidis, Nikolaos Tsapanos, Anastasios Tefas

Publication date: 3 September 2018

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.02.020

zbMATH Keywords

distributed computingdata clusteringkernel \(k\)-meansface clustering

Mathematics Subject Classification ID

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

Related Items (3)

Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach ⋮ Adaptive explicit kernel Minkowski weighted K-means ⋮ Distributed cooperative learning over time-varying random networks using a gossip-based communication protocol

Uses Software

• Hadoop
• Spark
• Graclus

Cites Work

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• Cooperative and penalized competitive learning with application to kernel-based clustering
• Kernel-based hard clustering methods in the feature space with automatic variable weighting
• Description of interest regions with local binary patterns
• The dip test of unimodality
• Spectral clustering based on local linear approximations
• A survey of kernel and spectral methods for clustering
• Theoretical foundations of the potential function method in pattern recognition learning

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