Using Otsu's Threshold Selection Method for Eliminating Terms in Vector Space Model Computation

Abstract

Visualization techniques have proved to be valuable tools to support textual data exploration. Dimensionality reduction techniques have been widely used to produce visual representation of document collections. Focusing on multidimensional projection techniques, good visual results are produced depending on how representative terms to discriminate the documents are chosen to compose the vector space model (VSM). To define a good VSM it is necessary to apply filters during the preprocessing in order to eliminate terms using their frequency. For that, the user must evaluate the term frequency histogram based on his/her expertise in the text subject and decide the threshold value for frequency cut. Usually it is a trial and error approach that requires the user to verify the quality of visual representation after each trial. In this paper, we propose an automatic approach that applies the Otsu's Threshold Selection Method for computing a threshold using a term frequency histogram. We conducted experiments that have shown our approach generates visual representations as good as those generated with a threshold obtained by trial and error approach. The contribution of our approach is that users with non expertise are able to generate good visual representations and the time to get a good threshold is decreased.