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Mining Association Rules from Structural Deltas of Historical XML Documents

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Advances in Knowledge Discovery and Data Mining (PAKDD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3056))

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Abstract

Previous work on XML association rule mining focuses on mining from the data existing in XML documents at a certain time point. However, due to the dynamic nature of online information, an XML document typically evolves over time. Knowledge obtained from mining the evolvement of an XML document would be useful in a wide range of applications, such as XML indexing, XML clustering. In this paper, we propose to mine a novel type of association rules from a sequence of changes to XML structure, which we call XML Structural Delta Association Rule (XSD-AR). We formulate the problem of XSD-AR mining by considering both the frequency and the degree of changes to XML structure. An algorithm, which is derived from the FP-growth, and its optimizing strategy are developed for the problem. Preliminary experiment results show that our algorithm is efficient and scalable at discovering a complete set of XSD-ARs.

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References

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, SINGAPORE

    Ling Chen, Sourav S. Bhowmick & Liang-Tien Chia

Authors
  1. Ling Chen
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  2. Sourav S. Bhowmick
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  3. Liang-Tien Chia
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Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Deakin University, VIC 3125, Australia

    Honghua Dai

  2. University of Illinois at Urbana-Champaign, 61801, Urbana, IL, USA

    Ramakrishnan Srikant

  3. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

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© 2004 Springer-Verlag Berlin Heidelberg

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Chen, L., Bhowmick, S.S., Chia, LT. (2004). Mining Association Rules from Structural Deltas of Historical XML Documents. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_55

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  • DOI: https://doi.org/10.1007/978-3-540-24775-3_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22064-0

  • Online ISBN: 978-3-540-24775-3

  • eBook Packages: Springer Book Archive

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