Semi-supervised graph clustering: a kernel approach

DOI10.1007/s10994-008-5084-4zbMath1472.68144OpenAlexW2610360795MaRDI QIDQ1009309

Inderjit S. Dhillon, Brian Kulis, Sugato Basu, Raymond J. Mooney

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-008-5084-4

zbMATH Keywords

graph clusteringsemi-supervised clusteringspectral learningkernel \(k\)-means

Mathematics Subject Classification ID

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

Related Items (17)

Local search for constrained graph clustering in biological networks ⋮ Learning a metric when clustering data points in the presence of constraints ⋮ A spectral approach to clustering numerical vectors as nodes in a network ⋮ \textit{Kernel cuts}: kernel and spectral clustering meet regularization ⋮ Semi-supervised clustering via multi-level random walk ⋮ Multi-task clustering via domain adaptation ⋮ Fast semi-supervised clustering with enhanced spectral embedding ⋮ Semi-supervised clustering with discriminative random fields ⋮ On the eigenvectors of \(p\)-Laplacian ⋮ Weakly supervised nonnegative matrix factorization for user-driven clustering ⋮ Efficient semi-supervised learning on locally informative multiple graphs ⋮ On a semi-superwized graph clustering problem ⋮ APPROXIMATE ALGORITHMS FOR GRAPH CLUSTERING PROBLEM ⋮ Semi-supervised concept factorization for document clustering ⋮ Link-aware semi-supervised hypergraph ⋮ Hierarchical graph embedding in vector space by graph pyramid ⋮ SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering

Uses Software

• KEGG
• Graclus

Cites Work

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• Correlation clustering
• Clustering with qualitative information
• Clustering Based on Conditional Distributions in an Auxiliary Space
• Approximation algorithms for classification problems with pairwise relationships
• Learning Theory and Kernel Machines
• Algorithmic Learning Theory
• Approximation, Randomization, and Combinatorial Optimization.. Algorithms and Techniques

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