Self-organizing maps for imputation of missing data in incomplete data matrices

Abstract

The problem of incomplete data matrices is repeatedly found in large databases, posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight, to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River, in Buenos Aires Province, Argentina, by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates, with no statistically significant differences from estimates made through professional criteria, proving thus to be a suitable time-saving imputation method.