Multi-level document classifications with self-organising maps

Title: Multi-level document classifications with self-organising maps
Creator: Ye, Huilin
Relation: Intelligent Data Engineering and Automated Learning: IDEAL 2005. 6th International Conference Brisbane, Australia, July 6-8, 2005 Proceedings. (Brisbane 6-8 July, 2005)
Publisher: Springer-Verlag
Resource Type: conference paper
Date: 2005
Description: 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.
Identifier: uon:617
Identifier: http://hdl.handle.net/1959.13/24554
Identifier: ISBN:354026972X
Language: eng

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