We propose a novel algorithm to estimate the similarity between a pair of eye movement sequences. The proposed algorithm relies on a straight-forward geometric representation of eye movement data. The algorithm is considerably simpler to implement and apply than existing similarity measures, and is particularly suited for exploratory analyses. To validate the algorithm, we conducted a benchmark experiment using realistic artificial eye move- ment data. Based on similarity ratings obtained from the proposed algorithm, we defined two clusters in an unlabelled set of eye movement sequences. As a measure of the al - gorithm's sensitivity, we quantified the extent to which these data-driven clusters matched two pre-defined groups (i.e., the 'real' clusters). The same analysis was performed using two other, commonly used similarity measures. The results show that the proposed al- gorithm is a viable similarity measure.
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