Clustering Algorithms and RFM Analysis Performed on Retail Transactions

Abstract

This paper investigates how clustering algorithms and Recency, Frequency, and Monetary value (RFM) analysis can be performed on online transactions to provide strategies for customer purchasing behaviors. Along with performing RFM analysis on the retail dataset, clustering algorithms such as Mean-shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Clustering, and K-Means were utilized. By comparing these clustering algorithms, we have found valuable customer groups based on RFM values.