A Self-Organizing Map Based Approach for Document Clustering and Visualization

In this paper, the clustering and visualization capabilities of the SOM, specifically tailored for the analysis of textual data, are reviewed and further developed. A novel clustering and visualization approach is proposed for the task of textual data mining. The proposed approach first transforms the document space into a multi-dimensional vector space by means of citation patterns. An intuitive and effective projection method, namely the Ranked Centroid Projection (RCP), is then applied in conjunction with a dynamic SOM model, the Growing Hierarchical Self-Organizing Map, which automatically produces document maps with various levels of details. The RCP is used both as a data analysis tool as well as a direct interface to the data. We also extend the RCP to address the problem of the incremental clustering of dynamic document collections. In a set of simulations, the proposed approach is applied to a synthetic data set and two real-world scientific document collections, to demonstrate its applicability.