IEEE Trans Neural Netw Learn Syst. 2017 Jun;28(6):1331-1344. doi: 10.1109/TNNLS.2016.2536104. Epub 2016 Mar 18.
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.
在本文中，我们引入了一种新的关联规则（AR）形式，它不需要对连续变量进行离散化，也不需要在规则的两侧使用区间。这种规则形式捕捉了变量之间的非线性关系，并为挖掘给定数据集隐藏的基本关系提供了一种替代的模式表示。我们将这种新的规则形式称为函数型关联规则（FAR）。我们提出了一种基于神经网络的、协作的、协同进化算法来进行 FAR 挖掘。该算法应用于合成和真实数据集，并对其性能进行了分析。实验结果表明，所提出的挖掘算法能够发现数据中的有效和基本的潜在关系。还与能够处理连续变量的两种最先进的 AR 挖掘算法进行了对比实验，以证明所提出方法的竞争性能。
