Applied Intelligence

, Volume 36, Issue 1, pp 108–118 | Cite as

Mining bridging rules between conceptual clusters

  • Shichao ZhangEmail author
  • Feng Chen
  • Xindong Wu
  • Chengqi Zhang
  • Ruili Wang


Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising.


Bridging rule Clustering Weighting Association rule Entropy 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Shichao Zhang
    • 1
    • 5
    Email author
  • Feng Chen
    • 2
  • Xindong Wu
    • 3
    • 4
  • Chengqi Zhang
    • 5
  • Ruili Wang
    • 6
  1. 1.Zhejiang Normal UniversityJinhuaChina
  2. 2.La Trobe UniversityMelbourneAustralia
  3. 3.Hefei University of TechnologyHefeiChina
  4. 4.University of VermontVermontUSA
  5. 5.Centre for Quantum Computation and Intelligent SystemsUniversity of TechnologySydneyAustralia
  6. 6.Massey UniversityPalmerston NorthNew Zealand

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