Game data mining: clustering and visualization of online game data in cyber-physical worlds

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dc.contributor.authorBraun, Peter
dc.contributor.authorCuzzocrea, Alfredo
dc.contributor.authorKeding, Timothy D.
dc.contributor.authorLeung, Carson K.
dc.contributor.authorPadzor, Adam G.M.
dc.contributor.authorSayson, Dell
dc.date.accessioned2018-01-29T16:41:11Z
dc.date.available2018-01-29T16:41:11Z
dc.date.issued2017
dc.descriptionP. Braun, A. Cuzzocrea, T.D. Keding, C.K. Leung, A.G.M. Padzor, D. Sayson. Game data mining: clustering and visualization of online game data in cyber-physical worlds. Procedia Computer Science, 112 (2017), pp. 2259-2268. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.abstractSince its debut in May 2016, Overwatch has quickly become a popular team-based online video game. Despite the popularity of Overwatch, many new players---who join the game unsure how to compete with the game’s veterans---feel overwhelmed with the vast knowledge required to properly play at higher skill levels. In this paper, a data mining algorithm is designed and developed for clustering and visualization of online game data at the cyber-physical world boundary. Scientifically, the algorithm uses affinity propagation for clustering and two-dimensional graphs for visualizing online game data. The algorithm analyzes the Overwatch game data for the discovery of new knowledge about current players and the clustering of data for each hero character. This knowledge enables the analysis of individual clusters and provides statistics that have a high correlation with winning player strategies. These statistics are expected to have a large influence on how a character is played, and thus can aid new players in learning their priorities as each hero character. In other words, the algorithm helps analyze the online game playing data, get insight about the grouping or clusters of players, and offer suggestions to new players of the game.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.citationP. Braun, A. Cuzzocrea, T.D. Keding, C.K. Leung, A.G.M. Padzor, D. Sayson. Game data mining: clustering and visualization of online game data in cyber-physical worlds. Procedia Computer Science, 112 (2017), pp. 2259-2268en_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2017.08.141
dc.identifier.urihttp://hdl.handle.net/1993/32866
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsopen accessen_US
dc.subjectdata miningen_US
dc.subjectclusteringen_US
dc.subjectvisual analyticsen_US
dc.subjectcluster visualizationen_US
dc.subjectcyber-physical worlden_US
dc.subjectonline gameen_US
dc.subjectapplicationsen_US
dc.subjectinnovative artificial intelligence technologiesen_US
dc.titleGame data mining: clustering and visualization of online game data in cyber-physical worldsen_US
dc.typeArticleen_US
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