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Understanding occasion sequences is an important facet of game analytics, since it is relevant to many participant modeling questions. This paper introduces a technique for analyzing event sequences by detecting contrasting motifs; the intention is to discover subsequences that are considerably more comparable to at least one set of sequences vs. different sets. In comparison with present strategies, our approach is scalable and capable of dealing with long event sequences. We utilized our proposed sequence mining approach to analyze player behavior in Minecraft, a multiplayer on-line sport that supports many forms of player collaboration. MINECRAFT RLCRAFT SERVERS As a sandbox sport, it offers gamers with a considerable amount of flexibility in deciding how to complete duties; this lack of objective-orientation makes the problem of analyzing Minecraft event sequences extra difficult than occasion sequences from more structured games. MINECRAFT RLCRAFT SERVERS Using our approach, we have been in a position to discover distinction motifs for many participant actions, despite variability in how totally different gamers achieved the same tasks. Furthermore, we explored how the extent of player collaboration affects the distinction motifs. Though this paper focuses on applications within Minecraft, our instrument, which we've made publicly obtainable along with our dataset, can be utilized on any set of recreation occasion sequences.