An analysis of multi-dimensional data containing emphasized items by self-organizing map and its application to sightseeing information analysis

One of the attractive features which a self-organizing map (SOM) possesses is a topology-preserving projection from the input layer to the competitive layer. Generally speaking, its correspondence is developed through training based on information representations of the applied multi-dimensional data. A developed feature map in the competitive layer enables us easy to understand some underlying rules visually. By the way, an analysis of Saga Prefectural sightseeing information by a SOM has been tried so far. According to the results of preceding studies, applied various topics are divided into several groups successfully. Nevertheless, there are some items not reflected in them at all. This fact implies that representations of the applied data are not appropriate to develop the feature map which we have intended in advance. Then, to overcome this tough problem, a simple idea to emphasize particular items depending on our interests is introduced as pre-synaptic processing. As a result of some computer simulations, it is confirmed that the developed feature maps are modified adaptively depending on the emphasized coefficient. And, it is concluded that the proposed simple method is effective.