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
High utility itemset (HUI) mining is a popular data mining task, which consists of discovering sets of items generating high profit in a transaction database. Recently, several efficient algorithms have been proposed for this task. But, most of them do not consider the on-shelf time periods of items, which thus lead to a bias toward items having more shelf time. Moreover, most algorithms cannot handle databases containing items with a negative unit profit, although this case is very common in real transaction databases. In this paper, we address both of these challenges by proposing a novel efficient algorithm named FOSHU (Faster On-Shelf High Utility itemset miner) to mine HUIs while considering on-shelf time periods of items, and items having positive and/or negative unit profit. An extensive experimental study with real-life datasets shows that the proposed algorithm can be up more than 1000 times faster and use up to 10 times less memory than the state-of-the-art algorithm TS-HOUN for this task. Moreover, experiments show that the proposed algorithm performs well on dense database and databases containing many time periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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