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Understanding event sequences is a vital aspect of recreation analytics, since it is relevant to many player modeling questions. This paper introduces a method for analyzing occasion sequences by detecting contrasting motifs; the goal is to discover subsequences which can be considerably more related to at least one set of sequences vs. different units. Compared to existing methods, our approach is scalable and capable of handling long occasion sequences. We utilized our proposed sequence mining method to investigate participant habits in Minecraft, a multiplayer on-line sport that supports many types of player collaboration. As a sandbox game, it supplies gamers with a considerable amount of flexibility in deciding how to complete duties; this lack of purpose-orientation makes the issue of analyzing Minecraft occasion sequences more challenging than event sequences from extra structured video games. Using our approach, we were in a position to discover contrast motifs for many player actions, despite variability in how different players accomplished the identical duties. Moreover, we explored how the extent of participant collaboration affects the distinction motifs. 30TT Although this paper focuses on applications inside Minecraft, our device, which we've got made publicly obtainable along with our dataset, can be utilized on any set of recreation event sequences.