基于演进向量量化聚类的增量模糊关联分类方法

为了提高动态数据集上模糊关联分类器（FAC）的建模效率，提出了一种基于演进向量量化（eVQ）聚类的增量模糊关联分类方法。首先，采用eVQ聚类算法增量更新数量属性上的高斯隶属度函数参数；然后，扩展早剪枝更新（UWEP）算法，使之适用于增量挖掘模糊频繁项；最后，以模糊相关度（FCORR）和分类规则前件长度为度量方式裁剪并更新模糊关联分类规则库。在4个UCI标准数据集上的实验结果表明，与批量模糊关联分类建模方法相比，所提方法能够在保证分类精度和解释性的前提下，减少模糊关联分类器的训练时间；基于eVQ的高斯隶属度函数的增量更新有助于提高动态数据集上模糊关联分类器的分类精度。

Incremental fuzzy associative classification method based on evolving vector quantization clustering algorithm

In order to improve the efficiency of building Fuzzy Associative Classifier (FAC) on the dynamic data sets, an incremental fuzzy associative classification method based on eVQ (evolving Vector Quantization) clustering algorithm was proposed. Firstly, eVQ clustering algorithm was adopted to incrementally update the parameters of Gauss membership functions of quantitative attributes. Secondly, Update With Early Pruning (UWEP) algorithm was extended to incrementally mine fuzzy frequent itemsets. Finally, Fuzzy CORRelation (FCORR) of Fuzzy Associative Classification Rule (FACR) and the length of antecedent of FACR were regarded as measures to prune and update fuzzy associative classification rule base. The experimental results on four UCI benchmark data sets show that compared with the batch fuzzy association classification modeling method, the proposed method can reduce the time of training the FAC in the premise of not decreasing the accuracy and interpretability. The Gauss membership function updating method based on eVQ clustering algorithm contributes to improve the classification accuracy of the FAC on the dynamic data sets.