Pengelompokan Produk Berdasarkan Frekuensi Pembelian Menggunakan K-Means pada Dataset Market
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Abstract
Product clustering based on sales patterns is an essential step in supporting decision-making within the retail sector, which often involves high transaction volumes. This study aims to cluster products based on purchase frequency using the K-Means algorithm applied to a market dataset. The research stages include data cleaning, feature normalization, and determining the optimal number of clusters using the Elbow method. The results indicate that K-Means successfully groups products into three main clusters: high, medium, and low purchase frequency. This information can be utilized to optimize inventory management, determine restocking priorities, and design more effective marketing strategies. Therefore, the application of K-Means demonstrates its capability to provide data-driven insights into understanding product demand characteristics.
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