Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking
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
Mining for frequent item sets can generate an overwhelming number of patterns, often exceeding the size of the original transactional database. One way to deal with this issue is to set filters and interestingness measures. Others advocate the use of constraints to apply to the patterns, either on the form of the patterns or on descriptors of the items in the patterns. However, typically the filtering of patterns based on these constraints is done as a post-processing phase. Filtering the patterns post-mining adds a significant overhead, still suffers from the sheer size of the pattern set and loses the opportunity to exploit those constraints. In this paper we propose an approach that allows the efficient mining of frequent item sets patterns, while pushing simultaneously both monotone and anti-monotone constraints during and at different strategic stages of the mining process. Our implementation shows a significant improvement when considering the constraints early and a better performance over Dualminer which also considers both types of constraints.
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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