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Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field
- Source :
- The Scientific World Journal, Vol 2013 (2013), The Scientific World Journal
- Publication Year :
- 2013
- Publisher :
- Hindawi Limited, 2013.
-
Abstract
- 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.
- Subjects :
- Article Subject
Computer science
lcsh:Medicine
Machine learning
computer.software_genre
lcsh:Technology
General Biochemistry, Genetics and Molecular Biology
Pattern Recognition, Automated
Knowledge extraction
Entropy (information theory)
Computer Simulation
Aerospace
Cluster analysis
lcsh:Science
General Environmental Science
Models, Statistical
business.industry
lcsh:T
lcsh:R
General Medicine
Missing data
Sample size determination
Data quality
Sample Size
Gray relational analysis
lcsh:Q
Artificial intelligence
Data mining
business
Aviation
computer
Algorithms
Research Article
Subjects
Details
- Language :
- English
- Volume :
- 2013
- Database :
- OpenAIRE
- Journal :
- The Scientific World Journal
- Accession number :
- edsair.doi.dedup.....01a72b20eef253ff20e57313975b488e