Designing unsupervised machine learning algorithms to explore user behaviour patterns for online games

从在线游戏中产生的大量时变和高维数据提供了新的 游戏行业和研究人员都有机会获得可操作的见解。这些数据可能 允许企业预测他们的收入，提高技术性能，以提高用户的在线游戏 体验，根据用户的反馈或兴趣开发新游戏，最重要的是，预测 玩家的行为对给定游戏的未来有影响。了解球员的行为至关重要 因为它允许识别有价值的参与者以及准备流失的参与者。 这个项目的目的是研究如何分析游戏玩家产生的数据， 行为模式是否可以识别。

The massive amount of time-varying and high dimensional data generated from online games provides new opportunity to both the gaming industry and researchers to derive actionable insights. These data potentially allow businesses to predict their revenues, improve technical performances to enhance users' online gaming experience, develop new games based on users feedback or interests, and, most importantly, predict the behaviour of players with implications for future of a given game. Understanding players' behaviour is crucial as it allows the identification of both valuable players and also those players that are ready to churn. The objective of this project is to investigate how data generated by the game players can be analyzed and whether behavioural patterns can be identified.