  This study presents a comprehensive analysis of user behavior and clustering
in Peace Elite, a popular mobile battle royale game, employing temporal and
static data mining techniques to uncover distinct player segments. Our
methodology encompasses time series K-means clustering, graph-based algorithms
(DeepWalk and LINE), and static attribute clustering, visualized through
innovative hybrid charts. Key findings reveal significant variations in player
engagement, skill levels, and social interactions across five primary user
segments, ranging from highly active and skilled players to inactive or new
users. We also analyze the impact of external factors on user retention and the
network structure within clusters, uncovering correlations between cluster
cohesion and player activity levels. This research provides valuable insights
for game developers and marketers, offering data-driven recommendations for
personalized game experiences, targeted marketing strategies, and improved
player retention in online gaming environments.
