Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support |
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| Authors: | Xiaowei Yan Chengqi Zhang Shichao Zhang |
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| Affiliation: | 1. VNU University of Science, Vietnam National University, Vietnam;2. School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK;3. Computer Science and Engineering Department, LNCT College, MP, India;4. Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, Delhi, India;5. Department of Computer Science & Engineering, AIACT&R, Delhi, India;6. Department of Computer Science and Engineering, GD-RCET, Bhilai, CG, India;7. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea |
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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. |
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