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Vertex Unique Labelled Subgraph Mining

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Research and Development in Intelligent Systems XXX (SGAI 2013)

Abstract

With the successful development of efficient algorithms for Frequent Subgraph Mining (FSM), this paper extends the scope of subgraph mining by proposing Vertex Unique labelled Subgraph Mining (VULSM). VULSM has a focus on the local properties of a graph and does not require external parameters such as the support threshold used in frequent pattern mining. There are many applications where the mining of VULS is significant, the application considered in this paper is error prediction with respect to sheet metal forming. More specifically this paper presents a formalism for VULSM and an algorithm, the Right-most Extension VULS Mining (REVULSM) algorithm, which identifies all VULS in a given graph. The performance of REVULSM is evaluated using a real world sheet metal forming application. The experimental results demonstrate that all VULS (Vertex Unique Labelled Subgraphs) can be effectively identified.

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Acknowledgments

The research leading to the results presented in this paper has received fund- ing from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 266208.

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Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool Ashton Building, Ashton Street, L69 3BX, Liverpool, UK

    Wen Yu, Frans Coenen, Michele Zito & Subhieh El Salhi

Authors
  1. Wen Yu
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  2. Frans Coenen
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  3. Michele Zito
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  4. Subhieh El Salhi
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Corresponding author

Correspondence to Wen Yu .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, West Sussex, United Kingdom

    Miltos Petridis

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© 2013 Springer International Publishing Switzerland

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Yu, W., Coenen, F., Zito, M., Salhi, S.E. (2013). Vertex Unique Labelled Subgraph Mining. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-02621-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02620-6

  • Online ISBN: 978-3-319-02621-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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