Co-Operative Coevolutionary Neural Networks for Mining Functional Association Rules
Co-Operative Coevolutionary Neural Networks for Mining Functional Association Rules
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DOI：
10.1109/tnnls.2016.2536104
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发表时间：
2017-06
影响因子：
10.4
通讯作者：
B. Wang;K. Merrick;H. Abbass
中科院分区：
文献类型：
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作者：
B. Wang;K. Merrick;H. Abbass
在本文中，我们介绍了一种新的形式的关联规则（AR），不需要离散化的连续变量或使用的间隔在任何一方的规则。这种规则形式捕捉变量之间的非线性关系，并为挖掘隐藏在给定数据集中的本质关系提供了另一种模式表示。我们将新的规则形式称为功能AR（FAR）。提出了一种新的基于神经网络的协同进化算法。该算法适用于合成和真实世界的数据集，并分析其性能。实验结果表明，该挖掘算法能够发现数据中有效的和本质的潜在关系。对比实验也进行了与两个国家的最先进的AR挖掘算法，可以处理连续变量，以证明所提出的方法的竞争性能。
In this paper, we introduce a novel form of association rules (ARs) that do not require discretization of continuous variables or the use of intervals in either sides of the rule. This rule form captures nonlinear relationships among variables, and provides an alternative pattern representation for mining essential relations hidden in a given data set. We refer to the new rule form as a functional AR (FAR). A new neural network-based, co-operative, coevolutionary algorithm is presented for FAR mining. The algorithm is applied to both synthetic and real-world data sets, and its performance is analyzed. The experimental results show that the proposed mining algorithm is able to discover valid and essential underlying relations in the data. Comparison experiments are also carried out with the two state-of-the-art AR mining algorithms that can handle continuous variables to demonstrate the competitive performance of the proposed method.