Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Wood inspection with non-supervised clustering
Machine Vision and Applications
Self-organizing feature maps for the vehicle routing problem with backhauls
Journal of Scheduling
Towards fair ranking of olympics achievements: the case of Sydney 2000
Computers and Operations Research
User modeling for personalized Web search with self-organizing map: Research Articles
Journal of the American Society for Information Science and Technology
User modeling for personalized Web search with self-organizing map: Research Articles
Journal of the American Society for Information Science and Technology
Anomaly detection in mobile communication networks using the self-organizing map
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Analysis of the convergence properties of topology preserving neural networks
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on organ donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting organ donation are related to perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.