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CCIC: Consistent Common Itemsets Classifier

  • Yohji Shidara
  • Atsuyoshi Nakamura
  • Mineichi Kudo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)

Abstract

We propose a novel approach which extracts consistent (100% confident) rules and builds a classifier with them. Recently, associative classifiers which utilize association rules have been widely studied. Indeed, the associative classifiers often outperform the traditional classifiers. In this case, it is important to collect high quality (association) rules. Many algorithms find only high support rules, because decreasing the minimum support to be satisfied is computationally demanding. However, it may be effective to collect low support but high confidence rules. Therefore, we propose an algorithm that produces a wide variety of 100% confident rules including low support rules. To achieve this goal, we adopt a specific-to-general rule searching strategy, in contrast to the previous many approaches. Our experimental results show that the proposed method achieves higher accuracies in several datasets taken from UCI machine learning repository.

Keywords

Association Rule Positive Instance Negative Instance Common Item 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-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yohji Shidara
    • 1
  • Atsuyoshi Nakamura
    • 1
  • Mineichi Kudo
    • 1
  1. 1.Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, HokkaidoJapan

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