{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:44:36Z","timestamp":1767339876975,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Indonesian Education Scholarship"},{"name":"Center for Higher Education Funding and Assessment"},{"name":"Indonesia Endowment Fund for Education"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>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\u2013temporal 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.<\/jats:p>","DOI":"10.3390\/mti9050042","type":"journal-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T20:10:27Z","timestamp":1746389427000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Structured Gaze Data Classification: The Gaze Data Clustering Taxonomy (GCT)"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8368-0843","authenticated-orcid":false,"given":"Yahdi","family":"Siradj","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciences, Melbourne Campus, La Trobe University, Melbourne, VIC 3086, Australia"},{"name":"Department of Multimedia Engineering, School of Applied Sciences, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-1409","authenticated-orcid":false,"given":"Kiki Maulana","family":"Adhinugraha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciences, Melbourne Campus, La Trobe University, Melbourne, VIC 3086, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8218-2343","authenticated-orcid":false,"given":"Eric","family":"Pardede","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciences, Melbourne Campus, La Trobe University, Melbourne, VIC 3086, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3758\/s13428-023-02150-0","article-title":"Noise Estimation for Head-Mounted 3D Binocular Eye Tracking Using Pupil Core Eye-Tracking Goggles","volume":"56","author":"Velisar","year":"2024","journal-title":"Behav. 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