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Integrating Association Rule Mining Algorithms with the F2 OODBMS

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Database and Expert Systems Applications (DEXA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2736))

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Abstract

In this paper, we integrate two association rule mining algorithms, Apriori and TRAND, with the F2 object-oriented database system (DBMS). The advantages of our integration are the following. Both algorithms do not need to maintain complicated data structures and use only database classes. Both algorithms do not need to manage the buffer since it is handled by the DBMS. Both algorithms store frequent itemsets in the database which can be retrieved later using the DBMS data manipulation language. In addition to that, the TRAND algorithm takes advantage of the transposed storage supported in F2. To compute the support of candidate itemsets, it applies logical AND on boolean attributes, implemented in F2 as vectors, and avoids scanning the database. This reduces significantly the number of block accesses and consequently the execution time.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, American University of Beirut, P.O. Box 11-0236, Beirut, Lebanon

    Lina Al-Jadir

Authors
  1. Lina Al-Jadir
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Editor information

Editors and Affiliations

  1. Gerstner Laboratory, Czech Technical University in Prague, Technická 2, 166 27, Prague 6, Czech Republic

    Vladimír Mařík

  2. Johannes Kepler University Linz, Altenberger Str. 69, 4040, Linz, Austria

    Werner Retschitzegger

  3. Faculty of Electrical Engineering, The Gerstner Laboratory, Czech Technical University in Prague, Technická 2, 166 27, Prague 6, Czech Republic

    Olga Štěpánková

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© 2003 Springer-Verlag Berlin Heidelberg

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Al-Jadir, L. (2003). Integrating Association Rule Mining Algorithms with the F2 OODBMS. In: Mařík, V., Retschitzegger, W., Štěpánková, O. (eds) Database and Expert Systems Applications. DEXA 2003. Lecture Notes in Computer Science, vol 2736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45227-0_71

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  • DOI: https://doi.org/10.1007/978-3-540-45227-0_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40806-2

  • Online ISBN: 978-3-540-45227-0

  • eBook Packages: Springer Book Archive

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