Title: Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data,
Abstract: The classification of time series data is a challenge common to all
data-driven fields. However, there is no agreement about which are the most
efficient techniques to group unlabeled time-ordered data. This is because a
successful classification of time series patterns depends on the goal and the
domain of interest, i.e. it is application-dependent.
In this article, we study free-to-play game data. In this domain, clustering
similar time series information is increasingly important due to the large
amount of data collected by current mobile and web applications. We evaluate
which methods cluster accurately time series of mobile games, focusing on
player behavior data. We identify and validate several aspects of the
clustering: the similarity measures and the representation techniques to reduce
the high dimensionality of time series. As a robustness test, we compare
various temporal datasets of player activity from two free-to-play video-games.
With these techniques we extract temporal patterns of player behavior
relevant for the evaluation of game events and game-business diagnosis. Our
experiments provide intuitive visualizations to validate the results of the
clustering and to determine the optimal number of clusters. Additionally, we
assess the common characteristics of the players belonging to the same group.
This study allows us to improve the understanding of player dynamics and churn
behavior.