A SOM Variant Based on the Wilcoxon Test for Document Organization and Retrieval

Abstract

A variant of the self-organizing maps algorithm is proposed in this paper for document organization and retrieval. Bigrams are used to encode the available documents and signed ranks are assigned to these bigrams according to their frequencies. A novel metric which is based on the Wilcoxon signed-rank test exploits these ranks in assessing the contextual similarity between documents. This metric replaces the Euclidean distance employed by the self-organizing maps algorithm in identifying the winner neuron. Experiments performed using both algorithms demonstrates a superior performance of the proposed variant against the self-organizing map algorithm regarding the average recall-precision curves.