EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization
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.
KeywordsSelf-organizing maps variants of SOM environmental conditions envSOM data mining pattern recognition
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