A dynamic composite approach for evaluating association rules
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
Association mining is one the many tasks in data mining. In this paper, we consider the problem of evaluating association rules, an integral post process in association mining. In the literature, different interestingness measures have been proposed to evaluate association rules. Given an association mining task, measures are selected according to 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 selections. In our work, we propose a novel approach that dynamically evaluates association rules according to a composite and collective effect of multiple measures. In essence, our approach makes use of neural networks along with back-propagation learning capability to determine the relative importance of measures in evaluating association rules. The effectiveness of our approach is shown through a set of empirical simulations. To the best of our knowledge, this is the first time that neural networks are applied to evaluating association rules.
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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 | 0.000 |
| 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 it