零售交易信息服务平台运行数据异常辨识研究

Research on Abnormal Identification of Operation Data of Retail Transaction Information Service Platform

异常数据的存在导致零售交易信息服务平台的用户体验不佳,因此需要设计零售交易信息服务平台运行数据异常辨识方法。文章首先采用网络爬虫技术采集平台数据,并对数据进行预处理,其次利用主成分分析法对数据进行特征提取,最后通过k-means聚类算法实现异常数据辨识。实验结果表明,该方法对数据异常辨识的精确度高、误差小、速度快。

Due to the existence of abnormal data,the user experience of retail transaction information service plaform is not good,so the method of identifying abnormal data of retail transaction information service platform is designed.The network crawler technology is used to collect platform data and preprocess the data.The principal component analysis method is used to extract the features of the data,and the k-means clustering algorithm is used to identify the abnormal data.The experimental results show that the proposed method has high accuracy,small error and high identification efficiency.