Visualization of Online-Game Players Based on Their Action Behaviors

Abstract EN

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.