A Hybrid Framework Using SOM and Fuzzy Theory for Textual Classification in Data Mining

This paper presents a hybrid framework combining self-organising map (SOM) and fuzzy theory for textual classification. Clustering using selforganizing maps is applied to produce multiple targets. In this paper, we propose that an amalgamation of SOM and association rule theory may hold the key to a more generic solution, less reliant on initial supervision and redundant user interaction. The results of clustering stem words from text documents could be utilised to derive association rules which designate the applicability of documents to the user. A four stage process is consequently detailed, demonstrating a generic example of how a graphical derivation of associations may be derived from a repository of text documents, or even a set of synopses of many such repositories. This research demonstrates the feasibility of applying such processes for data mining and knowledge discovery.