  The analysis of user behavior in digital games has been aided by the
introduction of user telemetry in game development, which provides
unprecedented access to quantitative data on user behavior from the installed
game clients of the entire population of players. Player behavior telemetry
datasets can be exceptionally complex, with features recorded for a varying
population of users over a temporal segment that can reach years in duration.
Categorization of behaviors, whether through descriptive methods (e.g.
segmention) or unsupervised/supervised learning techniques, is valuable for
finding patterns in the behavioral data, and developing profiles that are
actionable to game developers. There are numerous methods for unsupervised
clustering of user behavior, e.g. k-means/c-means, Non-negative Matrix
Factorization, or Principal Component Analysis. Although all yield behavior
categorizations, interpretation of the resulting categories in terms of actual
play behavior can be difficult if not impossible. In this paper, a range of
unsupervised techniques are applied together with Archetypal Analysis to
develop behavioral clusters from playtime data of 70,014 World of Warcraft
players, covering a five year interval. The techniques are evaluated with
respect to their ability to develop actionable behavioral profiles from the
dataset.
