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Mining bridging rules between conceptual clusters

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Abstract

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.

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Authors and Affiliations

  1. Zhejiang Normal University, Jinhua, China

    Shichao Zhang

  2. La Trobe University, Melbourne, Australia

    Feng Chen

  3. Hefei University of Technology, Hefei, China

    Xindong Wu

  4. University of Vermont, Vermont, USA

    Xindong Wu

  5. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Australia

    Shichao Zhang & Chengqi Zhang

  6. Massey University, Palmerston North, New Zealand

    Ruili Wang

Authors
  1. Shichao Zhang
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  2. Feng Chen
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  3. Xindong Wu
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  4. Chengqi Zhang
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  5. Ruili Wang
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Corresponding author

Correspondence to Shichao Zhang.

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Zhang, S., Chen, F., Wu, X. et al. Mining bridging rules between conceptual clusters. Appl Intell 36, 108–118 (2012). https://doi.org/10.1007/s10489-010-0247-y

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  • DOI: https://doi.org/10.1007/s10489-010-0247-y

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