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Non-segmented document clustering using self-organizing map and frequent max substring technique

Chumwatana, T., Wong, K.W. and Xie, H. (2009) Non-segmented document clustering using self-organizing map and frequent max substring technique. In: 16th International Conference on Neural Information Processing, ICONIP 2009, 1 - 5 Dec, Bangkok pp. 691-698.

Link to Published Version: http://dx.doi.org/10.1007/978-3-642-10684-2_77
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Abstract

This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring mining technique to improve the efficiency of information retrieval. The proposed technique appears to be a promising alternative for clustering non-segmented text documents. To illustrate the proposed technique, experiment on clustering the Thai text documents is presented in this paper. The frequent max substring mining technique is first applied to discover the patterns of interest called Frequent Max substrings or FM from the non-segmented Thai text documents. These discovered patterns are then used as indexing terms, together with their number of occurrences, to form a document vector. SOM is then applied to generate the document cluster map by using the document vector. As a result, the generated document cluster map can be used to find the relevant documents according to a user's query more efficiently.

Publication Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: Springer Verlag
Copyright: © 2009 Springer-Verlag
Notes: Appears in "Neural Information Processing" Lecture Notes in Computer Science, Volume 5864/2009 pp. 691-698
URI: http://researchrepository.murdoch.edu.au/id/eprint/1587
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