Intelligent Customer Behaviour Analysis in the Norwegian Market
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".