Multi-level document classifications with Self-Organising Maps

Abstract

The Self-Organising Map (SOM) is widely used to classify document collections. Such classifications are usually coarse-grained and cannot accommodate accurate document retrieval. A document classification scheme based on Multi-level Nested Self-Organising Map (MNSOM) is proposed to solve the problem. An MNSOM consists of a top map and a set of nested maps organised at different levels. The clusters on the top map of an MNSOM are at a relatively general level achieving retrieval recall, and the nested maps further elaborate the clusters into more specific groups, thus enhancing retrieval precision. The MNSOM was tested by a software document collection. The experimental results reveal that the MNSOM significantly improved the retrieval performance in comparison with the single SOM based classification. © Springer-Verlag Berlin Heidelberg 2005.