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Efficient Mining of Non-derivable Emerging Patterns

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Databases Theory and Applications (ADC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9093))

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Abstract

Emerging pattern mining is an important data mining task for various decision making. However, it often presents a large number of emerging patterns, most of which are not useful as their emergence are derivable from other emerging patterns. Such derivable emerging patterns most often are trivial in decision making. To enable mine the set of non-derivable emerging patterns for decision making, we employ deduction rules in identifying the set of non-derivable emerging patterns. We subsequently make use of a significance test to identify the set of significant non-derivable emerging patterns. Finally, we develop NEPs, an efficient framework for mining the set of non-derivable and significant non-derivable emerging patterns. Experimentally, NEPs is efficient, and the non-derivable emerging pattern set which is smaller than the set of all emerging patterns, shows potentials in trend prediction on a Twitter dataset.

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Author information

Authors and Affiliations

  1. School of Information Technology and Mathematical Science, University of South Australia, Adelaide, Australia

    Vincent Mwintieru Nofong, Jixue Liu & Jiuyong Li

Authors
  1. Vincent Mwintieru Nofong
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  2. Jixue Liu
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  3. Jiuyong Li
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Corresponding author

Correspondence to Vincent Mwintieru Nofong .

Editor information

Editors and Affiliations

  1. University of Queensland, Brisbane, Queensland, Australia

    Mohamed A. Sharaf

  2. Monash University, Clayton, Australia

    Muhammad Aamir Cheema

  3. The University of Melbourne, Melbourne, Australia

    Jianzhong Qi

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Nofong, V.M., Liu, J., Li, J. (2015). Efficient Mining of Non-derivable Emerging Patterns. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-19548-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19547-6

  • Online ISBN: 978-3-319-19548-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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