An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2002-07-18

AUTHORS

Akihiro Inokuchi , Takashi Washio , Hiroshi Motoda

ABSTRACT

This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing sub-structures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem.... More... »

PAGES

13-23

References to SciGraph publications

  • 1997-09. Levelwise Search and Borders of Theories in Knowledge Discovery in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2000. Extension of Graph-Based Induction for General Graph Structured Data in KNOWLEDGE DISCOVERY AND DATA MINING. CURRENT ISSUES AND NEW APPLICATIONS
  • 1999-10-22. Derivation of the Topology Structure from Massive Graph Data in DISCOVERY SCIENCE
  • Book

    TITLE

    Principles of Data Mining and Knowledge Discovery

    ISBN

    978-3-540-41066-9
    978-3-540-45372-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-45372-5_2

    DOI

    http://dx.doi.org/10.1007/3-540-45372-5_2

    DIMENSIONS

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