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dc.contributor.author Braun, Peter
dc.contributor.author Cuzzocrea, Alfredo
dc.contributor.author Keding, Timothy D.
dc.contributor.author Leung, Carson K.
dc.contributor.author Padzor, Adam G.M.
dc.contributor.author Sayson, Dell
dc.date.accessioned 2018-01-29T16:41:11Z
dc.date.available 2018-01-29T16:41:11Z
dc.date.issued 2017
dc.identifier.citation P. 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 en_US
dc.identifier.uri http://hdl.handle.net/1993/32866
dc.description P. 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.abstract Since 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.sponsorship Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject data mining en_US
dc.subject clustering en_US
dc.subject visual analytics en_US
dc.subject cluster visualization en_US
dc.subject cyber-physical world en_US
dc.subject online game en_US
dc.subject applications en_US
dc.subject innovative artificial intelligence technologies en_US
dc.title Game data mining: clustering and visualization of online game data in cyber-physical worlds en_US
dc.type Article en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.procs.2017.08.141


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