FIMUS: A framework for imputing missing values using co-appearance, correlation and similarity analysis
作者：
Highlights：
• A novel missing value imputation technique.
• Justification of the basic concepts of the technique through some empirical analyses.
• Experimentation on nine data sets, two evaluation criteria.
• Comparison with four existing techniques.
• A complexity analysis of all techniques.
摘要
•A novel missing value imputation technique.•Justification of the basic concepts of the technique through some empirical analyses.•Experimentation on nine data sets, two evaluation criteria.•Comparison with four existing techniques.•A complexity analysis of all techniques.
论文关键词：Data pre-processing,Data cleansing,Missing value imputation,Data mining,Data quality
论文评审过程：Received 6 May 2013, Revised 2 December 2013, Accepted 3 December 2013, Available online 11 December 2013.
论文官网地址：https://doi.org/10.1016/j.knosys.2013.12.005
