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首页> 外文期刊>Computational Intelligence and AI in Games, IEEE Transactions on >Clustering Game Behavior Data
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Clustering Game Behavior Data

机译：聚类游戏行为数据

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摘要

Recent years have seen a deluge of behavioral data from players hitting the game industry. Reasons for this data surge are many and include the introduction of new business models, technical innovations, the popularity of online games, and the increasing persistence of games. Irrespective of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics are pointed out.
机译：近年来，来自玩家的大量行为数据冲击了游戏行业。数据激增的原因很多，其中包括新业务模型的引入，技术创新，在线游戏的普及以及游戏持续性的提高。无论原因如何，行为数据的泛滥都带来了如何从中获取见解的问题。行为数据集可能很大，与时间有关并且是高维度的。群集提供了一种探索此类数据并发现可降低数据整体复杂性的模式的方法。因此，用于游戏玩家分析和游戏风格分析的聚类和其他技术已在新兴的游戏分析领域流行。但是，正确使用聚类技术需要专业知识，对游戏的理解对于评估结果至关重要。在本文中，我们将介绍游戏数据科学家，并提供一篇针对集群技术挖掘行为游戏数据的评论和教程。审查了几种算法，并显示了其应用示例。讨论了诸如功能标准化之类的关键主题，并指出了游戏分析中的开放性问题。

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