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.

Sensitivity analysis of biogeoclimatic projections for the outlier removals from 0% (no removals) to 32% (1-sigma), for the 2041-2060 period, in the Kamloops study area.

Sensitivity analysis of biogeoclimatic projections for the outlier removals from 0% (no removals) to 32% (1-sigma), for the 1961-1990 period, in the Kamloops study area.

Sensitivity analysis of biogeoclimatic projections for the outlier removals from 0% (no removals) to 32% (1-sigma), for the 1961-1990 period, in the Pemberton study area.

Sensitivity analysis of biogeoclimatic projections for the outlier removals from 0% (no removals) to 32% (1-sigma), for the 2041-2060 period, in the Pemberton study area.