Exploring clustering algorithms on spatial data in video games

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dc.contributor.authorFitsner, Elise
dc.date.accessioned2025-08-07T16:21:45Z
dc.date.available2025-08-07T16:21:45Z
dc.date.issued2025
dc.description.abstractThis project looks at how unsupervised machine learning (specifically, clustering data using a Gaussian mixture model) can be used to try to identify different player archetypes in Counter-Strike: Global Offensive, using only data that relates to the spatial behaviour of the players. We found that within each side, a player's individual playstyle and the style of the round they were playing seem to have had more influence on the clustering than which map they were playing on, and we were able to identify four distinct styles of play on each side. In future work, this approach could be applied to areas such as cheat detection, improving video game map design, and quantifying effective play patterns to support skill development and training.
dc.description.reviewstatusReviewed
dc.description.scholarlevelUndergraduate
dc.description.sponsorshipValerie Kuehne Undergraduate Research Awards (VKURA)
dc.identifier.urihttps://hdl.handle.net/1828/22561
dc.language.isoen
dc.publisherUniversity of Victoria
dc.subjectmachine learning
dc.subjectplayer typology
dc.subjectvideo games
dc.subjecthuman mobility
dc.subject.departmentDepartment of Computer Science
dc.titleExploring clustering algorithms on spatial data in video games
dc.typePoster

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