Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field
Joint Authors
Tian, Jing
Yu, Bing
Yu, Dan
Ma, Shilong
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-02
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Tian, Jing& Yu, Bing& Yu, Dan& Ma, Shilong. 2013. Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1033234
Modern Language Association (MLA)
Tian, Jing…[et al.]. Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field. The Scientific World Journal No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1033234
American Medical Association (AMA)
Tian, Jing& Yu, Bing& Yu, Dan& Ma, Shilong. Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1033234
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033234