Abstract
This chapter describes cluster evaluation, description, and interpretation for evaluating player profiles based on log files available from a game server. Calculated variables were extracted from these logs in order to characterize players. Using circular statistics, we show how measures can be extracted that enable players to be characterized by the mean and standard deviation of the time that they interacted with the server. Feature selection was accomplished using a correlation study of variables extracted from the log data. This process favored a small number of the features, as judged by the results of clustering. The techniques are demonstrated based on a log file data set of the popular online game Minecraft. Automated clustering was able to suggest groups that Minecraft players fall into. Cluster evaluation, description, and interpretation techniques were applied to provide further insight into distinct behavioral characteristics, leading to a determination of the quality of clusters, using the Silhouette Width measure. We conclude by discussing how the techniques presented in this chapter can be applied in different areas of serious games analytics.
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Acknowledgments
We would like to thank Masahiro Takatsuka who supplied the data used in this study, and Samuel Cornforth who assisted with decoding server logs, and provided details of gameplay in Minecraft.
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Cornforth, D.J., Adam, M.T.P. (2015). Cluster Evaluation, Description, and Interpretation for Serious Games. In: Loh, C., Sheng, Y., Ifenthaler, D. (eds) Serious Games Analytics. Advances in Game-Based Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-05834-4_6
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DOI: https://doi.org/10.1007/978-3-319-05834-4_6
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