A Study On Two-Stage Self-Organizing Map And Its Application To Clustering Problems

This paper presents a two-stage self-organizing map algorithm that we call two-stage SOM which combines Kohonen's basic SOM (BSOM) and Aoki's SOM with threshold operation (THSOM). In the first stage of two-stage SOM, we use BSOM algorithm in order to acquire topological structure of input data, and then we apply THSOM algorithm so that inactivated code vectors move to appropriate region reflecting the distribution of the input data. Furthermore, we show that two-stage SOM can be applied to clustering problems. Some experimental results reveal that two-stage SOM is effective for clustering problems in comparison with conventional methods. (c) 2007 Wiley Periodicals, Inc.