Visualization of Online‐Game Players Based on Their Action Behaviors

We propose a visualization approach for analyzing players′ action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and KeyGraph. CMDS is for discovering clusters of players who behave similarly. KeyGraph 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.