A Document Clustering Method with Natural Language Processing and Self-Organizing Maps on Database System for Clinical Cases

Abstract

In this article, we propose a document clustering method with Natural Language Processing and Self-Organizing Maps (SOM) on our Web based DBMS (Data Base Management System) for clinical cases. The system is implemented in Java language on the Internet server, and all documents of clinical cases in the database are written in Japanese. We tested the system ability to evaluate for the correct clustering of clinical cases, that each document has 30 attributes in an input-vector for SOM-learning. For the result of this ability test, it shows that 73.9% of clustering datasets are correct answers. Thus, it seems that our method is available to search the similar cases in the database.