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  • COMPARING K-MEANS AND HIERARCHICAL CLUSTERING TECHNIQUES FOR MOBA GAME DATA ANALYSIS
  • https://doi.org/10.71284/asc.2025175Copy DOI Icon

COMPARING K-MEANS AND HIERARCHICAL CLUSTERING TECHNIQUES FOR MOBA GAME DATA ANALYSIS

  • Sep 1, 2025
  • Sema Yıldırım
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Abstract

This study applied various clustering algorithms to a multivariate dataset from a MOBA (Multiplayer On- line Battle Arena) game. The results revealed similarities between players, which were examined in detail using visual analysis techniques. Clustering algorithms are one of the most effective machine learning methods for discovering structural similarities between observations. In this study, K-means and Hierarchi- cal clustering algorithms, both unsupervised learning methods, were used to identify meaningful groups, and various analyses were performed. The results were compared, the effects on data segmentation were evaluated, and the findings were made clearer. The data analysis process began with converting categor- ical variables into numerical format using appropriate preprocessing techniques and scaling all variables using the StandardScaler method. Then, K-means and Hierarchical clustering algorithms were successfully applied to obtain different segmentations of the player data, and the characteristics of each segment were examined in depth. The resulting clusters were reduced to a two-dimensional plane using Principal Com- ponent Analysis (PCA), and the structural differences between the clusters formed by the algorithms were clearly visualized. The analysis results showed that while similar clusters were produced by both K-means and Hierarchical algorithms, different structural patterns were also identified. Visualizations made with PCA facilitated a better understanding of these separations. The suitability of the number of clusters and the performance of the algorithms were measured using the Silhouette score, and it was found that the K-means algorithm provided more consistent and effective results. The findings of this study demonstrate that clustering algorithms can capture meaningful patterns in dynamic and high-dimensional data, such as those from a MOBA game. Future studies could increase the generalizability of these findings by repeating similar clustering analyses on larger and more heterogeneous datasets. Additionally, comparisons with den- sity-based algorithms like DBSCAN would allow for a deeper evaluation of the algorithms’ performance in different data structures.

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