PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Self-Organizing Maps
A supervised training algorithm for self-organizing maps for structures
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A taxonomy of Self-organizing Maps for temporal sequence processing
Intelligent Data Analysis
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Comparative analysis of power consumption in university buildings using envSOM
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
| Hi-index | 0.00 |
In this paper, we present a new approach suitable for analysis of large data sets, conditioned on the environment. Mainly, the envSOM algorithm consists of two consecutive trainings of the self-organizing map. In the first phase, a SOM is trained using every available variable, but only those which characterize the environment are used to compute the winner unit. Therefore, this phase produces an accurate model of the environment. In the second phase, a new SOM is initialized appropriately with information from the codebooks of the first SOM. The new SOM uses all the variables for winner selection. However, in this case the environmental variables are kept fixed and only the remaining ones are involved in the update process. A model of the whole data set influenced by the environmental conditions is obtained in this second phase. The result of this algorithm represents a probability function of a data set, given the environment information. Therefore, it could be very useful in the analysis of processes which have close dependencies on environmental conditions.