基于K-means聚类算法的人事管理异常数据识别和自动处理系统

Personnel management abnormal data recognition and automatic processing system based on K-means clustering algorithm

针对人事管理异常数据影响人事管理水平的问题,设计基于K-means聚类算法的人事管理异常数据识别和自动处理系统。利用全局优化K-means聚类算法,对人事管理数据进行聚类处理。该算法搜寻高密度的人事管理数据作为初始聚类中心,将人事管理数据聚类为多个簇。利用K-means聚类算法构建人事管理数据的自回归模型,确定人事管理数据参量的转移概率序列。转移概率序列非聚类簇中的数据时,对应数据即为人事管理异常数据识别结果。采用指数加权移动平均数方法自动修正处理所识别的人事管理异常数据。系统测试结果表明,所设计系统能够有效识别人事管理考勤数据、薪资数据中的异常数据,能够自动修正异常数据,使人事管理数据恢复正常。

In response to the problem of abnormal data in personnel management affecting the level of personnel management,a personnel management abnormal data recognition and automatic processing system based on K-means clustering algorithm is designed.Utilize global optimization K-means clustering algorithm to cluster personnel management data.This algorithm searches for high-density personnel management data and uses it as the initial clustering center to cluster the personnel management data into multiple clusters.Using the K-means clustering algorithm,construct an autoregressive model for personnel management data and determine the transition probability sequence of personnel management data parameters.When transferring probability sequences to non clustered data,the corresponding data is the identification result of abnormal personnel management data.Using the index weighted moving average method,automatically correct and process identified abnormal personnel management data.The system test results show that the designed system effectively identifies abnormal data in personnel management attendance data and salary data,can automatically correct abnormal data,and restore personnel management data to normal.