GRID Oriented Implementation of Self-organizing Maps for Data Mining in Meteorology

We study the efficiency of different alternatives for a scalable parallel implementation of the self-organizing map (SOM) in the GRID environment of variable resources and communications. In particular, we consider an application of data mining in Meteorology, which involves databases of high-dimensional atmospheric patterns. In this work, we focus in network partitioning alternatives, analyzing their advantages and shortcomings in this framework. As a conclusion we obtain that there is no optimal alternative, and a combination (hybridization) of algorithms is required for a GRID application.