The nearest neighbor algorithm of filling missing data based on cluster analysis

摘要：

　　Missing data universally exists in various research fields and it results in bad computational performance and effcet.In order to improve the accuracy of filling in the missing data,a filling missing data algorithm of the nearest neighbor based on the cluster analysis is proposed by this paper.After clustering data analysis,the algorithm assigns weights according to the categories and improves calculation formula and filling value calculation based on the MGNN (Mahalanobis-Gray and Nearest Neighbor algorithm) algorithm.The experimental results show that the filling accuracy of the method is higher than traditional KNN algorithm and MGNN algorithm.

关键词： Grey mcorrelation Mahalanobis distance Cluster analysis Nearest neighbor alorithm Maximum

作者: Zhang Chi Jin Kai Fong Hong-cai Yang Ting

作者单位: Department of mathematics and Computer,Wuhan Polytechnic University Electronic banking department,de Department of mathematics and Computer,Wuhan Polytechnic University Wuhan,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 2579-2582

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）