Visualization of Online-Game Players Based on Their Action Behaviors
Visualization of Online-Game Players Based on Their Action Behaviors
复制标题
DOI：
10.1155/2008/906931
复制
发表时间：
2008-01
期刊：
影响因子：
--
通讯作者：
R. Thawonmas;Keita Iizuka
中科院分区：
文献类型：
--
作者：
R. Thawonmas;Keita Iizuka
我们提出了一个可视化的方法来分析球员的行动行为。所提出的方法包括两个可视化技术：经典的多维缩放（CMDS）和关键图。CMDS用于发现行为相似的玩家集群。关键图用于解释兴趣集群中玩家的动作行为。为了减少用于计算CMDS输入的矩阵的维数，我们利用我们最近提出的时间序列减少技术。我们的可视化方法使用在线游戏的日志进行评估，其中根据Bartle的分类法发现了三种玩家类型，即成就者、探索者和社交者。
We propose a visualization approach for analyzing players' action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and Key Graph. CMDS is for discovering clusters of players who behave similarly. Key Graph is for interpreting action behaviors of players in a cluster of interest. In order to reduce the dimension of matrices used in computation of the CMDS input, we exploit a time-series reduction technique recently proposed by us. Our visualization approach is evaluated using log of an online game where three-player types according to Bartle's taxonomy are found, that is, achievers, explorers, and socializers.