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
Gold Price, Neural Networks and Genetic Algorithm
Computational Economics
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
A comparative analysis of an extended SOM network and K-means analysis
International Journal of Knowledge-based and Intelligent Engineering Systems
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
Selecting the right MBA schools - An application of self-organizing map networks
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
HEp-2 cell images classification based on textural and statistic features using self-organizing map
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Computers & Mathematics with Applications
Hi-index | 12.05 |
Per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It seeks to quantify the Earth's biological capacity required to support human activity. Self-organizing maps (SOM) is a machine learning method that can be used to explore patterns in large and complex datasets for linear and non-linear patterns. This study uses SOM to model and cluster the EF of 140 nations. The results show that major variables affecting a nation's EF are related to the nation's world system position (WSP), GDP, urbanization level, export as a percent of the GDP, services intensity, and literacy rate. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional environmental data.