An improving pruning technique with restart for the Kohonen self-organizing feature map

Presents a pruning technique developed for the one-dimensional Kohonen self-organizing feature map (SOM) to be applied in clustering and classification problems. Its innovative aspect is the combined proposition of a penalty term, a clustering measure, a delayed pruning activation and a restarting phase. The proposed algorithm (PSOM) always guides to a reduced architecture capable of representing the data set. We compare the PSOM with the original SOM applying them to three different classification problems. The results show that the PSOM is able to present superior performance in all cases.