Open AccessClustering Steam User Behavior Data using K-Prototypes Algorithm
Author(s) -
Kiefer Stefano Ranti,
Kelvin Salim,
Abba Suganda Girsang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1367/1/012018
Subject(s) - cluster analysis , computer science , behavioral pattern , cluster (spacecraft) , telemetry , behavioral modeling , order (exchange) , human–computer interaction , data mining , artificial intelligence , software engineering , computer network , telecommunications , finance , economics
The use of user telemetry to gather player behavioral data on video games can be very beneficial to game developers with a certain business model. With the help of user telemetry in game development, it can provide access to data on user behavior from installed game clients platform such as Steam. These behavioral data can be used to find out the Steam user behavioral patterns on playtime distributions that can be studied by developers in order to have a deeper understanding of the behaviors of their players. In this study, the data are clustered using the k-prototypes algorithm, a combination of k-means and k-modes algorithm that can be used to cluster mixed attributes. The result shows that the clusters represent the types and preferences of the players.