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The Scientific World Journal
Volume 2013, Article ID 720392, 10 pages
Research Article

Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field

State Key Laboratory of Software Development Environment, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

Received 7 March 2013; Accepted 9 April 2013

Academic Editors: Y.-P. Huang, P. Melin, M. F. G. Penedo, and D. Rodriguez

Copyright © 2013 Jing Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A large number of scientific researches and industrial applications commonly suffer from missing data. Some inappropriate techniques of missing value treatment compromise data quality, which detrimentally influences the knowledge discovery. In this paper, we propose a missing data completion method named CBGMI. Firstly, it separates the nonmissing data instances into several clusters by excluding the missing-valued entries. Then, it utilizes the entropy of the proximal category for each incomplete instance in terms of the similarity metric based on gray relational analysis. Experiments on UCI datasets and aerospace datasets demonstrate that the superiority of our algorithm to other approaches on validity.