Effective feature construction by maximum common subgraph sampling

DOI10.1007/s10994-010-5193-8zbMath1237.68162OpenAlexW1974551701WikidataQ57923338 ScholiaQ57923338MaRDI QIDQ413859

Leander Schietgat, Fabrizio Costa, Jan Ramon, Luc De Raedt

Publication date: 8 May 2012

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

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/270068

zbMATH Keywords

chemoinformaticsfeature generationstructure-activity learningsubgraph mining

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Biochemistry, molecular biology (92C40)

Related Items (3)

On maximum common subgraph problems in series-parallel graphs ⋮ Mining closed patterns in relational, graph and network data ⋮ A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

Uses Software

• AFGen
• gSpan

Cites Work

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• 10.1162/153244303321897681
• ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns

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