Bibliographic record
Abstract
Clustering is an unsupervised machine learning technique in which data is grouped into segments such that observations within a segment are similar while those across segments differ. The technique has a wide range of applications including customer segmentation, anomaly detection, and feature engineering. While traditional methods applied to cross-sectional data employ static features and distance-based algorithms such as k-means or hierarchical clustering, time-series data is additionally complex due to its sequential structure. Aggregating transactional data into tabular form often neglects dynamic behavioural patterns, underscoring the need for methodologies that explicitly account for temporal dependencies. This research note reviews and compares different approaches to time-series clustering, including summary-statistic transformations, “native” methods such as Dynamic Time Warping (DTW), feature-based frameworks like RFM and Catch22, and deep learning models like Deep Temporal Clustering (DTC). Using a transactional dataset from Victory Farms, a sustainable aquaculture business in Kenya, we apply all methods to real-world customer data. The results highlight how time-series clustering yields richer insights than cross-sectional approaches, while also serving as a practical case study that combines theory, code, and application.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".