A new approach to hybrid SOM implementations for text classification

This paper analyses several recent treatises on hybridised self-organising map (SOM) theory. Each article proposes a solution to expedite the SOM mapping process and provides more accurate results within a shorter response time via hybridisation: including utilisation of Bayesian classification techniques; an interactive associative search and exploration tool; and the use of a hierarchical organization of tiered SOM's with input derived via auto-associative feedforward neural network technology. 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.