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

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成果类型:
期刊论文、会议论文
作者:
Chi Zhang;Jin Kai;Hong Cai Feng;Tin Yang
作者机构:
[Fong H.-C.; Kai J.; Zhang C.; Yang T.] Department of mathematics and Computer, Wuhan Polytechnic University, Wuhan, China
语种:
英文
关键词:
Cluster analysis;Grey mcorrelation;Mahalanobis distance;Maximum;Nearest neighbor alorithm
期刊:
Applied Mechanics and Materials
ISSN:
1660-9336
年:
2013
卷:
347-350
页码:
2324-2328
会议名称:
2013 International Conference on Precision Mechanical Instruments and Measurement Technology, ICPMIMT 2013
会议时间:
25 May 2013 through 26 May 2013
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
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...

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