Self-Organizing Maps
Vector Quantization and Projection Neural Network
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Handwritten Signature Verification Based on Neural "Gas" Based Vector Quantization
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
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We studied heart failure mortality and hospitalization of N=7,788 subjects in the Digitalis Intervention Group (DIG) clinical trial. Cases were defined as subjects with NewYork Heart Association (NYHA) class III-IV symptoms, while controls were defined as subjects with NYHA class I-II symptomatology. Controls were propensity matched with cases using logits from logistic regression, best winning nodes for neural gas and self-organizing maps, and k-means cluster analysis. Cox proportional hazards (PH) regression models were ran to determine the all-cause mortality and hospitalization hazard ratio (HR) for having NYHA functional class III-IV. Unmatched data resulted in a mortality HR of 1.28 (95% CI, 1.17-1.41), while logit-based propensity matching resulted in a mortality HR of 1.29 (95% CI, 1.15-1.44). When neural gas (NG) was used for propensity matching with normalized and standardized features, the mortality HR was 1.34 (95% CI, 1.19-1.50)and 1.05 (95% CI, 0.94-1.17), respectively. Propensity matching with self-organized maps (80M) and normalized and standardized features yielded mortality HRs of 1.31 (95% CI, 1.16-1.46) and 1.05 (95% CI, 0.94-1.17), respectively. Crisp K-means cluster-based matching performed worse and biased the HRs towards the null value of HR=I. The strongest influence of matching was observed for NG when normalized features were used.