An enhancing dynamic self-organizing map for data clustering

This paper presents a novel growing self-organizing map which features incremental learning, dynamic network structure and good visualization ability. It allows for on-line and continuous learning on both static and evolving data distributions. The experiments are carried out on some benchmark data sets for vector quantisation and clustering. Compared with the GSOM method, our results show that this new model can achieve better or comparable performance in real-world data sets.