Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

  • Authors:

  • Xiaowei Yan;Chengqi Zhang;Shichao Zhang

  • Affiliations:

  • Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway NSW 2007, Australia;Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway NSW 2007, Australia;Institute of Logics, Zhongshan University, PR China and School of Computer Science and Information Technology, Guangxi Normal University, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only.