Comparison of K-Means and K-Medoids in Product Clustering Using RFM and Frequent Closed Itemset

Retailers managing large stock-keeping unit (SKU) catalogues need a compact, auditable view of how individual products behave in order to plan replenishment, assortment and promotions. We present an interpretable analytics pipeline that derives SKU-level recency, frequency and monetary (RFM) features from one year of fashion-retail transactions (40,760 SKUs; 101,144 orders), segments the catalogue with prototype-based clustering under explicit internal validation, reads the segments alongside a co-purchase layer obtained by closed-itemset mining, and delivers the result to category managers through a deployed web application. Model selection is made explicit rather than assumed: K-Means and K-Medoids are compared at matched cluster counts on the Silhouette coefficient and the Davies–Bouldin Index (DBI). The two methods perform comparably at k=2, and K-Means is clearly superior at every larger cluster count; the selected configuration (K-Means, k=4) attains a Silhouette of 0.577 and a DBI of 0.624, against 0.522 and 0.762 for the best K-Medoids configuration. The resulting segments separate a small group of fast-moving items from a long tail of low-frequency products, and profiling them on the monetary axis shows that the separation tracks value contribution: 7.1 per cent of the clustered SKUs account for 28.8 per cent of monetary contribution, and the 19 fast-movers contribute roughly eleven times the per-SKU average. We set out how each segment maps to replenishment, assortment and promotion decisions, together with the validation each mapping would require before adoption. Two scope conditions should be read with the results: clustering uses the recency and frequency axes, with monetary value reported as a descriptive attribute of the segments rather than as a clustering input, and the co-purchase layer rests on very low support because baskets in this catalogue average 1.16 SKUs, so the itemsets are exploratory anchors rather than association rules.