MeAR-CP: Evaluation of the Quality of Association Rules Using Constraint Programming
Bibliographic record
Abstract
Association rules mining is one of the most relevant techniques in data mining.Aimed at identifying interesting connections and associations among groups of items or products within extensive transactional databases.However, this technique can yield too many rules, among which some are irrelevant and/or redundant.Thus, may present obstacles for the decision-maker.This Highlights the importance and challenge of evaluating extracted knowledge to define the most interesting association rules.In order to address this issue, we presented a constraint programming approach to evaluate the relevance of association rules.Our MeAR-CP approach involves filtering association rules using metrics such as IR, Cosine, Lift, Kulc as constraints solved by Choco constrain programming tools, and proposed our metric called 'Score'.The experiments are conducted on various datasets from the UCI Machine Learning Repository.We evaluate both time and rules.The results obtained from our experiments underscore the effectiveness of our approach in reducing irrelevant and redundant rules within an effective timeframe.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".