摘要
在异步数据正常传输的过程中,可能会出现异常数据或者无效数据,为了保证数据正常传输,需要对异步数据进行无损检测。提出一种改进Kmeans算法的交互式多属性网络异步数据的无损检测方法。首先提取目标网络异步数据向量特征,并赋予其不同的权重值;然后将给定范围内数据点最多的位置设定为初始中心点,并迭代聚类;最终利用改进Kmeans算法对交互式多属性网络异步数据进行聚类检测,实现对网络异步数据的无损检测。仿真证明,上述方法能有效提高数据聚类的准确率,提升异步数据无损检测精度,具有较高的可行性。
The abnormal data or invalid data may appear during normal transmission of asynchronous data.In or-der to ensure the normal transmission of data,it is necessary to detect the asynchronous data nondestructively.This paper focused on a nondestructive detection method for asynchronous data in interactive multi-attribute network based on improved K-means algorithm.Firstly,the vector features of asynchronous data in target network were extracted.And then,different weight values were given to the vector features.Then,the location with the largest number of data points in given range was set as the initial center point,and the iterative clustering was carried out.Finally,the im-proved K-means algorithm was used to cluster and detect the asynchronous data in interactive multi-attribute network and thus to achieve the non-destructive detection for the asynchronous network data.Simulation results show that the proposed method can effectively improve the accuracy of data clustering and non-destructive detection of asynchronous data.This method has higher feasibility.
作者
孟庆春
李爽
MENG Qing-chun;LI Shuang(School of Information Engineering,Zhengzhou University of Industrial Technology,Xinzheng 451100,China)
出处
《计算机仿真》
北大核心
2020年第1期398-401,共4页
Computer Simulation
关键词
交互式处理
多属性网络
异步传输数据
无损检测
Interactive processing
Multi-attribute network
asynchronous transmission data
Non-destructive de-tection
