Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support
- Publication Type:
- Journal Article
- Expert Systems with Applications, 2009, 36 (2 PART 2), pp. 3066 - 3076
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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. © 2008 Elsevier Ltd. All rights reserved.
Please use this identifier to cite or link to this item: