Visualization of the Critical Patterns of Missing Values in Classification Data

摘要

The patterns of missing values are important for assessing the quality of a classification data set and the validation of classification results. The paper discusses the critical patterns of missing values in a classification data set: missing at random, uneven symmetric missing, and uneven asymmetric missing. It proposes a self-organizing maps (SOM) based cluster analysis method to visualize the patterns of missing values in classification data.