Rough possibilistic meta-clustering of retail datasets
Bibliographic record
Abstract
In this paper, we develop a new meta-clustering approach using possibility and rough set theories to handle imperfection in real-world retail datasets. Our proposal is a soft meta-clustering approach that provides a framework for handling uncertainty in the belonging of an object to different clusters. The soft meta-clustering approach is based on the k-modes algorithm devoted for categorical data. Possibility theory is used to represent the uncertainty between objects and clusters through possibilistic membership degrees. Rough set theory is applied to indicate clusters with rough boundaries. The meta-clustering consists of double clustering a retail dataset that contains customer and product data. The meta-clustering is improved by the application of the possibility and rough set theories. An initial clustering of the customer data is performed. Then, a second clustering of product data using the results of the first clustering is applied. The two clustering schemes then evolve iteratively affecting each other recursively. We detail our results and describe the structure of the final clusters of customers and products to prove the effectiveness of our proposal.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".