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中文摘要：
基于构造型神经网络引入一种新的数据流聚类相似性函数,并根据滑动窗口模型数据流聚类的特点,定义了平均覆盖和重叠覆盖等概念,进而提出基于构造型神经网络的滑动窗口模型数据流聚类算法.该算法可以降低计算量,提高聚类速度.大规模无线电监测数据聚类实验验证了该算法的有效性.
英文摘要：
The article introduces a new clustering similarity based on the constructive neural networks.The conceptions of average cover and over cover are defined to meet the need of sliding windows model data stream clustering,and then a clustering algorithm for sliding windows model data stream is present based on the constructive neural networks.It can be found that the calculation can be lessened and the speed of clustering can be faster with the use of this algorithm.The experimental results of large-scale communication datasets demonstrate its efficiency.
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