Show simple item record Leung, Carson Kanke, Felix Cuzzocrea, Alfredo 2019-01-08T18:30:34Z 2019-01-08T18:30:34Z 2018 2019-01-08T07:45:02Z en
dc.identifier.citation C.K. Leung, F. Kanke, A. Cuzzocrea. Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki. Procedia Computer Science, 126 (2018), pp. 831-840 en_US
dc.description.abstract In the current era of big data, high volumes of a wide variety of data of different veracity are generated at a high velocity in many real-life applications. Embedded in these big data is valuable information or knowledge. This calls for data science solution for discovering knowledge from the big data. A rich source of big data is game data. In this article, we focus on the board game of Go, which is a popular two-player strategic board game. Due to its popularity, many people are studying sequences of moves in games (i.e., joseki). However, with high volumes of the game data, manual solution or complex automatic solution for joseki may not be practical. Hence, in this article, we present a simple automatic data science solution for discovering interesting sequences of moves in joseki for the board game Go. Evaluation results show the benefits and practicality of using our solution in data analytics 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.subject data science en_US
dc.subject data mining en_US
dc.subject Go en_US
dc.subject joseki en_US
dc.subject board game en_US
dc.subject game mining en_US
dc.subject prefix tree en_US
dc.subject pruning en_US
dc.title Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki en_US
dc.type Article en_US

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