Algorithms for clustering data
Algorithms for clustering data
Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising
Data Mining and Knowledge Discovery
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
A visual tool for tracing users' behavior in Virtual Environments
Proceedings of the working conference on Advanced visual interfaces
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Visualizing Competitive Behaviors in Multi-User Virtual Environments
VIS '04 Proceedings of the conference on Visualization '04
Visualization of online-game players based on their action behaviors
International Journal of Computer Games Technology - Networking for Computer Games
Player modeling using self-organization in tomb raider: underworld
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Gameplay analysis through state projection
Proceedings of the Fifth International Conference on the Foundations of Digital Games
The challenge of designing scientific discovery games
Proceedings of the Fifth International Conference on the Foundations of Digital Games
Placing a value on aesthetics in online casual games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
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Visual data mining is a powerful technique allowing game designers to analyze player behavior. Playtracer, a new method for visually analyzing play traces, is a generalized heatmap that applies to any game with discrete state spaces. Unfortunately, due to its low discriminative power, Playtracer's usefulness is significantly decreased for games of even medium complexity, and is unusable on games with continuous state spaces. Here we show how the use of state features can remove both of these weaknesses. These state features collapse larger state spaces without losing salient information, resulting in visualizations that are significantly easier to interpret. We evaluate our work by analyzing player data gathered from three complex games in order to understand player behavior in the presence of optional rewards, identify key moments when players figure out the solution to the puzzle, and analyze why players give up and quit. Based on our experiences with these games, we suggest general principles for designers to identify useful features of game states that lead to effective play analyses.