Efficient indirect association discovery using compact transaction databases
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Bibliographic record
Abstract
Abstract — An indirect association is a special type of negative association that relates two items via a mediator. The two items in an indirect association are rarely present together, but each of them occurs frequently together with the mediator. In this paper, we propose HI-mine*, an innovative optimization of the previously developed HI-mine algorithm for fast extracting indirect associations. This optimization is based on a novel strategy for compressing a transaction database into a Super Compact Transaction Database, which dramatically reduces not only the number of transactions in the database, but also the memory requirement for storing frequent-item projections in mining indirect associations. Our experimental results show that the HI-mine * algorithm is effective and efficient, and improves the performance of indirect association mining significantly. Index Terms — Data mining, association rule, indirect associa-tion, algorithm. I.
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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