What is data imputation using self-organizing maps?

What is data imputation using self-organizing maps?

Data imputation using self-organizing maps is a technique used in data preprocessing to fill in missing values in a dataset. Self-organizing maps (SOMs) are unsupervised machine learning algorithms that create a low-dimensional representation of the input data. In the context of data imputation, SOMs are trained on the available data to learn the underlying patterns and relationships. Once trained, the SOM can be used to predict the missing values based on the patterns observed in the existing data. This imputation method helps to ensure that the dataset is complete and suitable for further analysis or modeling.