A Comparative Analysis of K – Means And K – Medoids for Smartphone Clustering - M. Arif Fadillah; Kurniabudi Kurniabudi; Dodo Zaenal Abidin (ISB Atma Luhur, 2026)
The rapid growth of the smartphone industry has increased market complexity, making objective segmentation more challenging. The objective of this study is to conduct a comparative evaluation of the K - Means and K - Medoids methods in grouping smartphone sales data based on multiple attributes, namely memory, storage, rating, selling price, and original price. Unlike prior studies, this research conducts a direct comparison using the same dataset and multiple evaluation metrics. A systematic data mining approach is implemented through the CRISP-DM framework, covering the stages of data understanding, preprocessing, modeling, and evaluation. The dataset comprises 3,114 smartphone instances, which are grouped into three clusters (k = 3), with performance measured using the Silhouette Coefficient and Davies–Bouldin Index (DBI). Based on the evaluation metrics, K - Medoids exhibits superior cluster cohesion with a Silhouette score of 0.344, exceeding that of K - Means (0.313). Conversely, K - Means demonstrates slightly better separation, as shown by its lower Davies–Bouldin Index (1.061 versus 1.079). Even so, K - Medoids is generally preferred due to its resilience to outliers and its consistency in producing stable clustering outcomes. These findings provide insights to support data-driven decision-making in smartphone market segmentation.