A two-stage self-organizing map with threshold operation for data classification

This paper presents a two-stage self-organizing map algorithm with threshold operation. Kohonen´s basic SOM algorithm (BSOM) is simple and effective for data classification problems of high-dimensional data. But inactivated cells appear for specific input data and it causes to decline the ability of data classification. In order to solve this problem, BSOM with threshold operation (THSOM) was proposed recently. The THSOM algorithm, however, tends to loose topological structure of input data. Our two-stage self-organizing map algorithm inherits both good points of BSOM and THSOM. Numerical simulations reveal that the two-stage SOM can achieve small clustering error and high topology preservation in comparison with BSOM and THSOM.