抄録
In order to visualize the electronic data of law, a twodimensional map model is proposed. The proposed system firstly performs morphological analysis of each law to generate input vectors, which are composed of extracted keywords from the laws. The proposed efficient Self-Organizing Maps method uses restricted region search and dimensionality reduction by utilizing the specific characteristic of the law data vectors, which have a lot of sparse elements. The obtained map represents the nature of relations of the laws relation nature which would be intuitively understandable to the users. To confirm the effectiveness of the proposed system, evaluation experiments are done by degree of similarity between nearest law using 150 law data from the year 2005 to 2009. The computation time of the proposed method is decreased into 64-98% that of the conventional learning method of SOM, and increase the precision of classification of target law data.
