Fast Winner Search for SOM-Based Monitoring and Retrieval of High-Dimensional Data

Self-Organizing Maps (SOMs) are widely used in engineering and data-analysis tasks, but so far rarely in very large-scale problems. The reason is the amount of computation: while small SOMs can be computed starting from the basic principles, rapid computation of large maps of high-dimensional data requires special methods. Winner search, nding the position of a data sample on the map, is the computational bottleneck: comparison between the data vector and all of the model vectors of the map is required. In this paper a method is proposed for reducing the amount of computation by restricting the search to certain small-dimensional subspaces of the original space. The method is suitable for applications in which the map can be computed oo-line, for instance in data monitoring, classi-cation, and information retrieval. In a case study with the WEBSOM system that organizes text document collections on a SOM, the amount of computation was reduced to about 14% of the original, and even to 6.6% when approximations were utilized.