Towards Structured Gaze Data Classification: The Gaze Data Clustering Taxonomy (GCT)

Gaze data analysis plays a crucial role in understanding human visual attention and behaviour. However, raw gaze data is often noisy and lacks inherent structure, making interpretation challenging. Therefore, preprocessing techniques such as classification are essential to extract meaningful patterns and improve the reliability of gaze-based analysis. This study introduces the Gaze Data Clustering Taxonomy (GCT), a novel approach that categorises gaze data into structured clusters to improve its reliability and interpretability. GCT classifies gaze data based on cluster count, target presence, and spatial–temporal relationships, allowing for more precise gaze-to-target association. We utilise several machine learning techniques, such as k-NN, k-Means, and DBScan, to apply the taxonomy to a Random Saccade Task dataset, demonstrating its effectiveness in gaze classification. Our findings highlight how clustering provides a structured approach to gaze data preprocessing by distinguishing meaningful patterns from unreliable data.