基于数据挖掘的人力资源数据缺失值填补方法

Method of Filling Missing Values in Human Resource Data Based on Data Mining

现有人力资源数据缺失值填补方法均方根误差大、填补命中率低等问题。提出一种基于数据挖掘的人力资源数据缺失值填补方法。采用分裂Bregman迭代算法消除人力资源数据中存在的噪声,根据人力资源数据的时间序列特征,挖掘数据中存在的隐藏变量。根据特征对缺失值进行检测。通过FCMSI算法根据缺失值检测结果对缺失值进行填补,采用平均比率法首次填充人力资源数据,通过模糊C均值聚类算法对填充后的数据进行聚类处理,其次在协同过滤思想的基础上进一步对人力资源数据的缺失值进行填补。实验结果表明,所提方法的均方根误差小、填补命中率高。

Due to the large root mean square error and low filling hit rate of missing value filling method of human resources data.This paper proposes a missing value filling method of human resources data based on data mining.The split Bregman iterative algorithm is used to eliminate the noise in human resources data,and the hidden variables in human resources data are mined according to the time series characteristics of human resources data.The missing value is detected according to the characteristics.The fcmsi algorithm is used to fill in the missing value is detected according to the missing value detection results.The average ratio method is used to fill in the human resource data for the first time.The filled data is clustered by the fuzzy c-means clustering algorithm.Secondly,the missing value of human resource data is further filled on the basis of the idea of collaborative filtering.Experimental results show that the proposed method has small root mean square error and high filling hit rate.