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Granule Mining and Its Application for Network Traffic Characterization

  • Bin Liu
  • Yuefeng Li
  • Kewen Wang
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 16)

Abstract

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

Keywords

Decision Rule Association Rule Association Mapping Decision Table Granular Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bin Liu
    • 1
  • Yuefeng Li
    • 1
  • Kewen Wang
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia
  2. 2.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia

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