Automatic clustering of eye gaze data for machine learning

Eye gaze patterns or scanpaths of subjects looking at art while answering questions related to the art have been used to decode those tasks with the use of certain classifiers and machine learning techniques. Some of these techniques require the artwork to be divided into several Areas or Regions of Interest. In this paper, two ways of clustering the static visual stimuli - k-means and the density based clustering algorithm called OPTICS - were used for this purpose. These algorithms were used to cluster the gaze points before classification. The classification success rates were then compared. While it was observed that both k-means and OPTICS gave better success rates than manual clustering, which is itself higher than chance level, OPTICS consistently gave higher success rates than k-means given the right parameter settings. OPTICS also formed clusters that look more intuitive and consistent with the heat map readings than k-means, which formed clusters that look unintuitive and less consistent with the heat map.