Integrating static and dynamic hierarchical clustering and its application to retail segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on an approach to address large-scale data gathered from heterogeneous sources by integrating static and dynamic data in hierarchical clusterization, and its application to the analysis of retail branches. Traditionally, branch clustering analysis has relied on static information and the utilization of statistical measures to extract relevant features from the dynamic data and incorporate them into the static dataset; however, the application of this approach presents several challenges. This research proposes a solution that addresses these disadvantages while aiming to maintain the success achieved when applying unsupervised machine learning algorithms. The paper presents an approach based on the integration of static attributes and time series data in a hierarchical clustering manner that enables the identification of key performance indicators and offers insight into factors that influence branch performance over time. The results show the potential to optimize resource allocation, inventory management, and customer service strategies. The proposed approach is demonstrated using retail shop data from a Spanish telecommunications company (Grupo Masmovil), highlighting its effectiveness in enhancing cluster profiling and offering meaningful insights beyond the prevailing approaches. This method presents significant enrichment for clustering analysis that can be applied to different domains. • Integrated static and time-series data for comprehensive retail branch clustering. • Successfully applied the approach to a Spanish telecommunications company data. • Addressed big data variety by proposing hierarchical clustering for data integration. • The proposed approach extends beyond retail and is applicable to diverse domains.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it