This study presents an integrated framework combining clustering (K-Means, DBSCAN), dimensionality reduction (PCA, UMAP), and association rule mining (Apriori, Eclat) to extract actionable insights from retail data. Using a Kaggle dataset of over 1,000 customer transactions, we identify three distinct customer segments: high-spending youth, older frequent buyers, and budget-conscious middle-aged shoppers. We link these segments to product affinities, such as the association between blouses and jewelry. Unlike prior studies treating these methods separately, our integrated approach enables cluster-specific marketing strategies such as personalized bundling and influencer-driven campaigns. We validate cluster robustness through multi-algorithm consensus and demonstrate UMAP’s effectiveness over PCA in capturing nonlinear demographic-spending relationships. The study also discusses limitations such as parameter sensitivity and data granularity, offering insights for future research and practical applications.

Basket analysis using association rules is a key data mining technique for identifying relationships between frequently purchased items. This paper ranks and analyzes the most critical steps in basket analysis, detailing their implementation in R. We discuss rule generation, evaluation metrics, visualization, and their impact on business decision-making. Additionally, we compare Apriori, Eclat, and FP-Growth algorithms, highlighting their advantages and limitations.