基于蚁狮优化算法和FP-Growth的改进关联规则挖掘算法

Dawei Dong, Z. Ye, Yu Cao, Shiwei Xie, Fengwen Wang, Wei Ming
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引用次数: 7

摘要

在大数据时代,从海量数据中发现知识发挥着重要作用,FP-Growth算法是学习关联规则最成功的方法之一。虽然FP-Growth算法只需要扫描两次,但对于大型数据集来说,它的效率很低。一些元启发式优化算法,如粒子群优化算法(PSO)、免疫算法等,已经在解决这一问题上做出了努力,这些算法优于传统的FP-Growth算法,表现出较强的性能。然而,粒子群算法容易陷入局部最优。提出了一种新的蚁群优化算法(ALO),该算法具有全局寻优、鲁棒性好、收敛精度高等优点,已广泛应用于天线阵综合、综合工艺规划、调度等工程领域。在蚁狮优化算法的基础上,提出了一种新的关联规则提取算法。该方法采用了一种新的基于置信度和支持度的适应度模式,避免了FP-Growth算法中部分不必要的搜索过程,提高了搜索最优解的效率。为了评估该方法的有效性,在不同的数据集上进行了实验,并将实验结果与其他经典的元启发式算法进行了比较,实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth
Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.
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