Learning Using a Self-building Associative Frequent Network

  • Jin-Guk Jung
  • Mohammed Nazim Uddin
  • Geun-Sik Jo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)


In this paper, we propose a novel framework, called a frequent network, to discover frequent itemsets and potentially frequent patterns by logical inference. We also introduce some new terms and concepts to define the frequent network, and we show the procedure of constructing the frequent network. We then describe a new method LAFN (Learning based on Associative Frequent Network) for mining frequent itemsets and potentially patterns, which are considered as a useful pattern logically over the frequent network. Finally, we present a useful application, classification with these discovered patterns from the proposed framework, and report the results of the experiment to evaluate our classifier on some data sets.


Association Rule Class Label Frequent Pattern Frequent Itemsets Transaction Database 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Jin-Guk Jung
    • 1
  • Mohammed Nazim Uddin
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent e-Commerce Systems Laboratory, 253 Yonghyun-Dong, Nam-Gu, Incheon, 402-751Korea
  2. 2.School of Computer Engineering, Inha University, 253 Yonghyun-Dong, Nam-Gu, Incheon, 402-751Korea

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