Missing Data Imputation Techniques
Missing Data Imputation Techniques
复制标题
DOI：
10.1504/ijbidm.2007.015485
复制
发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
Qinbao Song;M. Shepperd
中科院分区：
文献类型：
--
作者：
Qinbao Song;M. Shepperd
Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data. Fortunately, Missing Data Imputation Techniques (MDITs) can be used to improve data quality. However, no one method MDIT can be used in all conditions, each method has its own context. In this paper, we introduce the MDITs to the KDD and machine learning communities by presenting the basic idea and highlighting the advantages and limitations of each method.
