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A Novel Self-Organizing Map for Text Document Organization

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be revealed. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically that could discover the relationships among documents in both perspectives. The proposed algorithm will first cluster a set of training documents using classical SOM algorithm. We then identify the topics of each cluster and use them to evaluate the criteria on expanding the map. We applied the algorithm on medium-size datasets and obtained promising result.