A constructive and hierarchical self-organizing model in a non-stationary environment

Several related self-organizing neural models have been proposed to enhance the flexibility of self-organizing maps. In our studies, these models depend on the pre-definition of several thresholds which are used as guidance of neural behaviors for specific data sets. However, it is not trivial to determine those thresholds in a non-stationary environment. When a proper threshold has been determined, this threshold may not be suitable for the future. Therefore, in this paper, we compare the dynamic adaptive self-organizing hybrid (DASH) model with the growing neural gas (GNG) model by introducing several different initial thresholds to test their feasibility. Our experiments show that the DASH model is more stable and practicable for document clustering in a non-stationary environment since DASH adjusts its behavior not only by modifying its parameters but also by an adaptive structure.