Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation

Multiple imputation of missing data using self organizing map ensembles

Processing Missing Values with Self-Organized Maps

This paper introduces modifications of Self-Organizing Maps allowing imputation and classification of data containing missing values. The robustness of the proposed modifications is shown using experimental results of a standard data set. A comparison to modified Fuzzy cluster methods [Timm et al., 2002] is presented. Both methods performed better with available case analysis compared to comple...

A Dynamical Implementation of Self-organizing Maps

The standard learning algorithm for self-organizing maps (SOM) involves the two steps of a search for the best matching neuron and of an update of its weight vectors in the neighborhood of this neuron. In the dy-namical implementation discussed here, a competitive dynamics of laterally coupled neurons with diiusive interaction is used to nd the best-matching neuron. The resulting neuronal excit...

Missing Values with iterative imputation

In this paper, the author designs an efficient method for imputing iteratively missing target values with semiparametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in e...

Self Organizing Maps: A Robust Implementation

Methods for visualizing multidimensional data are of great interest in computer science and engineering. One popular technique is selforganizing map, a type of neural network, that uses machine learning algorithms to map multidimensional data to a two-dimensional surface. They are widely used for exploratory data analysis and visualization and have been used to perform clustering and classifica...