Evaluating Association Rules by Quantitative Pairwise Property Comparisons
Why this work is in the frame
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Bibliographic record
Abstract
Evaluating association rules is an integral post process in association rule mining. Association rules are examined by measures for their interestingness. Different interestingness measures have been proposed. Given an association rule mining task, measures are assessed and selected against a set of user-specified properties. However, in practice, due to the subjectivity and imperfection in property specifications, it is a non-trivial task to make appropriate measure selection. In this work, we propose a novel measure selection approach that makes use of the Analytic Hierarchy Process (AHP), a scheme for making complex decisions. Our approach captures a user's desired requirements quantitatively in an application domain to assess interestingness measures. It detects inconsistencies in property specifications, and is invariant to the number of association rules to be evaluated. The effectiveness of our approach is shown through case studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it