Clustering Algorithms Analysis Based on Arcade Game Player Behavior


Shamsudin, Daniel and Leow, Meng Chew and Ong, Lee Yeng (2022) Clustering Algorithms Analysis Based on Arcade Game Player Behavior. 2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). pp. 122-125. ISSN 2831-7203

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The purpose of this study is to investigate the feasibility of using different clustering algorithms in grouping arcade game data for player behavior profiling. Using 3 clustering algorithms namely K-Means, Hierarchical Agglomerative Clustering, and DBSCAN, recorded game data for 6 games were clustered and the performance of each clustering algorithm was measured and compared. K-Means was shown to produce the highest quality and well formed clusters among all other algorithms used, and it also scored the highest on two of the evaluation metrics used. This study definitely answered the question regarding the utilization of different clustering algorithm with the use of arcade game data. Further studies are needed in order to generalize the idea of player profiling on games as a whole, with no regards in genres.

Item Type: Article
Uncontrolled Keywords: Clustering algorithm, player behavior analysis, player archetype
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure (General) > GV1-1860 Recreation. Leisure > GV1199-1570 Games and amusements
Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 10 Jan 2023 01:40
Last Modified: 10 Jan 2023 01:40


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