Whereas, an improved ART2 neural network clustering algorithm is proposed to realize the clustering of dynamic samples, and the simulation results are given out at the same time.
鉴于此，本文又提出了一种改进的ART2网络学习算法来实现动态样本的聚类，同时给出了该方法的实验仿真结果。
This paper describes the relevant concepts and presents a model of CBR based on dynamic data stream mining, and gives an improved clustering algorithm of data stream.
首先阐述了相关概念，接着提出了一种基于动态数据流挖掘的案例推理模型，其中动态数据流挖掘算法采用改进的数据流聚类算法。
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