Market basket analysis identifies item patterns in data, commonly used in retail to understand customer shopping habits and inform business decisions. Challenges arise with large, high-dimensional datasets. We propose a framework for market basket analysis using dimension reduction and clustering on data from a major Norwegian grocery retailer. This reduces complexity, allowing us to visualize and group data using clustering. The aim is to group similar transactions for association rule mining on a smaller subset. Our research goal is to develop a mobile application for customer grouping and pattern analysis. We apply K-means for grouping and Apriori for rule mining. We evaluate multiple dimension reduction techniques and cluster validation methods. This proved challenging due to dataset intricacies. Results favour t-SNE for dimension reduction, as it effectively separates transactions. Apriori yields many trivial rules, but 'Vegetables/potatoes' emerges as significant. A business case is needed for actionable rules. A better product hierarchy for detailed cluster analysis is also beneficial. Future work should explore improved dimension reduction and clustering assessment methods. The full code can be downloaded from: https://github.com/YousIA/ConsumerAnalytics.

Intelligent Customer Behaviour Analysis in the Norwegian Market