A Study on Two-stage Self-Organizing Map suitable for Clustering Problems

This paper presents a two-stage self-organizing map algorithm what 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