A Hybrid Parallel SOM Algorithm for Large Maps in Data-Mining

We propose a method for a parallel implementation of the Self-Organizing Map (SOM) algorithm, widely used in data-mining. We call this method Hybrid in the sense that it combines the advantages of the common network-partition and data-partition approaches, and is particularly effective when dealing with large maps. Based on the fact that a global topological ordering of the map is achieved in a short period of time, the proposed method obtains this ordering during the initial epochs using the Batch Data-Partition algorithm. Here we calculate the input data histogram over the map, based on which the map is segmented and the respective input vectors redistributed equally. From now on, until new segmentation each node only processes their subset of samples in their region of the map. Our experimental results show an average speed-up of 1.27 compared to the classical Batch data-partition method, while maintaining the topological information of the maps.