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Mining intelligent knowledge from a two-phase association rules mining

Author

Listed:
  • Yuejin Zhang
  • LingLing Zhang
  • Ying Liu
  • Yong Shi

Abstract

Association rule mining generates large quantities of rules, but not all of them are useful for decision making. In order to find the genuine useful knowledge for decision making, we propose an intelligent knowledge discovery model which is a new purpose-oriented approach based on a second order mining from association rules. More specifically, our model consists of two phases. In the first phase, proper objective measures are selected according to the user's goal. In the second phase, we define a new concept of rule utility measure as the subjective evaluation which incorporates user's goal, expert's experience, and domain knowledge. By doing so, the intelligent knowledge, which can support special strategies can be obtained. Experiments on two real world databases validate the effectiveness of our new model.

Suggested Citation

  • Yuejin Zhang & LingLing Zhang & Ying Liu & Yong Shi, 2010. "Mining intelligent knowledge from a two-phase association rules mining," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 2(4), pages 403-419.
    • Handle: RePEc:ids:ijdmmm:v:2:y:2010:i:4:p:403-419
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    Cited by:

    1. Jianping Li & Xiaolei Sun & Fei Wang & Dengsheng Wu, 2015. "Risk integration and optimization of oil-importing maritime system: a multi-objective programming approach," Annals of Operations Research, Springer, vol. 234(1), pages 57-76, November.

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