This paper is devoted to the novel method of automated detection of common eye gaze movement patterns by mining the data recorded in eye-tracking-based experiments. For this, a model of aggregated scanpath is proposed that represents a fuzzy set of all possible eye gaze trajectories found in the experimental data. In contrast to the traditional methods of aggregation, no averaging is used to avoid information loss. Instead, the belonging function determines the probability of each particular trajectory. The constructed fuzzy scanpath is then filtered and automatically analyzed by applying methods of network science. For this, the fixations (eye gaze stops) are represented as network nodes and saccades (eye gaze jumps) are mapped to network links. For the network composed, modularity is calculated utilizing the Louvain method of community detection. In the case of eye gaze data, modularity represents saccadic cycles, which can be mapped to the cycles of cognitive processing. Thereby, the common perception structure is retrieved. To support all the analysis steps, we proposed corresponding scalable visualization tools based on our visual analytics platform SciVi. We demonstrate the viability of our approach by analyzing the data obtained from the real-world eye-tracking-based experiment from the Digital Humanities application domain. Preliminary experiment results are discussed along with the efficacy of the proposed methods.
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