Application of clustering algorithms in eye gaze visualizations

Abstract

Eye tracking devices generate enormous amount of data, which requires a well-balanced approach to selective visualization of the data. This approach involves employing some data clustering algorithm. Most of the traditional algorithms, however, are too slow as well as inadequately deterministic to be applied to eye gaze data. This paper describes our software implementation of two modifications of the clustering algorithm suitable for visualization of eye gaze data. Such visualization greatly facilitates data analysis by grouping the individual samples into more meaningful units referred to as gaze fixations.