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A Closed Frequent Subgraph Mining Algorithm in Unique Edge Label Graphs

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

Problems such as closed frequent subset mining, itemset mining, and connected tree mining can be solved in a polynomial delay. However, the problem of mining closed frequent connected subgraphs is a problem that requires an exponential time. In this paper, we present ECE-CloseSG, an algorithm for finding closed frequent unique edge label subgraphs. ECE-CloseSG uses a search space pruning and applies the strong accessibility property that allows to ignore not interesting subgraphs. In this work, graph and subgraph isomorphism problems are reduced to set inclusion and set equivalence relations.

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Author information

Authors and Affiliations

  1. Preparatory School of Science and Technology, P.O. Box 218, 23000, Annaba, Algeria

    Nour El Islem Karabadji

  2. LabGED Laboratory, Badji Mokhtar University, P.O. Box 12, 23000, Annaba, Algeria

    Nour El Islem Karabadji & Hassina Seridi

  3. Aalto University, School of Science, P.O. Box 12200, 00076, Espoo, FI, Finland

    Sabeur Aridhi

Authors
  1. Nour El Islem Karabadji
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  2. Sabeur Aridhi
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  3. Hassina Seridi
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Corresponding author

Correspondence to Sabeur Aridhi .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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© 2016 Springer International Publishing Switzerland

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El Islem Karabadji, N., Aridhi, S., Seridi, H. (2016). A Closed Frequent Subgraph Mining Algorithm in Unique Edge Label Graphs. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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