Finding density-based subspace clusters in graphs with feature vectors

DOI10.1007/s10618-012-0272-zzbMath1260.68356OpenAlexW2159633885MaRDI QIDQ1944987

Thomas Seidl, Brigitte Boden, Stephan Günnemann

Publication date: 28 March 2013

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-012-0272-z

zbMATH Keywords

networksgraph clusteringdense subgraphs

Mathematics Subject Classification ID

Graph theory (including graph drawing) in computer science (68R10) Pattern recognition, speech recognition (68T10)

Related Items (1)

Mining communities and their descriptions on attributed graphs: a survey

Uses Software

• Inc-cluster
• SA-cluster

Cites Work

• \(k\)-core architecture and \(k\)-core percolation on complex networks
• Managing and mining graph data
• A simple solution to the k‐core problem

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