A Comparative Analysis Of K-Means And K-Medoids Algorithms For Steam Game Clustering Based On Popularity, Price, And Playtime
Perbandingan Algoritma K-Means Dan K-Medoids Untuk Klasterisasi Game Pada Platform Steam Berdasarkan Popularitas, Harga, Dan Waktu Bermain
DOI:
https://doi.org/10.21070/ups.11841Keywords:
K-Means, K-Means Clustering, K-Medoids, Data Mining, clustering, Games, machine learningAbstract
Steam’s large and heterogeneous game catalog cannot be adequately described by genre alone because games with similar genres may differ in market reach, pricing, and player engagement. This study clusters Steam games using estimated owners, price, and average lifetime playtime, while comparing K-Means with K-Medoids implemented through FasterPAM. A four-stage filtering process reduced 136,080 raw records to 22,659 eligible games. The clustering features were transformed using Yeo-Johnson and standardized before evaluating K values from 2 to 10. K=3 was selected because it produced the highest Silhouette Score. K-Medoids achieved a Silhouette Score of 0.2922, slightly higher than K-Means at 0.2913, although its Davies-Bouldin Index was higher. K-Medoids also produced a more balanced cluster distribution and was selected as the primary model. Kruskal-Wallis tests showed significant differences across all features, producing three segments: Popular-Premium-High Engagement, Budget/F2P, and Niche-Low Engagement. Overall, K-Medoids provided a suitable representation of the analyzed Steam game population.
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