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Efficient Mining of Non-derivable Emerging Patterns

  • Vincent Mwintieru NofongEmail author
  • Jixue Liu
  • Jiuyong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)

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.

Keywords

Frequent patterns Emerging patterns Non-derivable emerging patterns Significance test 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vincent Mwintieru Nofong
    • 1
    Email author
  • Jixue Liu
    • 1
  • Jiuyong Li
    • 1
  1. 1.School of Information Technology and Mathematical ScienceUniversity of South AustraliaAdelaideAustralia

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