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
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
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on green consumption in Kuwait. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and non-linear patterns. The results show that major variables affecting green consumption are related to altruistic values, environmental concern, environmental knowledge, skepticism towards environmental claims, attitudes toward green consumption, and intention to buy green products. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.