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.
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Ye, H. (2005). Multi-level Document Classifications with Self-organising Maps. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_48
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DOI: https://doi.org/10.1007/11508069_48
Publisher Name: Springer, Berlin, Heidelberg
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