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Understanding occasion sequences is an important side of game analytics, since it is related to many player modeling questions. This paper introduces a technique for analyzing occasion sequences by detecting contrasting motifs; the aim is to find subsequences which might be considerably more similar to one set of sequences vs. different units. In comparison with present strategies, our approach is scalable and able to handling lengthy event sequences. We applied our proposed sequence mining strategy to research player behavior in Minecraft, a multiplayer online recreation that helps many forms of player collaboration. As a sandbox recreation, it supplies gamers with a considerable amount of flexibility in deciding how to complete duties; this lack of goal-orientation makes the issue of analyzing Minecraft event sequences extra challenging than occasion sequences from more structured games. Using our strategy, we had been ready to discover contrast motifs for a lot of participant actions, regardless of variability in how different gamers completed the same duties. Furthermore, we explored how the extent of participant collaboration affects the contrast motifs. Although this paper focuses on applications within Minecraft, our software, which now we have made publicly accessible along with our dataset, can be used on any set of recreation event sequences. I'm bonnie and you are