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Summary: Feature-Based Projections for Effective Playtrace Analysis
Yun-En Liu, Erik Andersen, Richard Snider, Seth Cooper, and Zoran Popovi´c
Center for Game Science
Department of Computer Science & Engineering, University of Washington
{yunliu, eland, sniderrw, scooper, zoran}@cs.washington.edu
ABSTRACT
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, Play-
tracer'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
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