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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.057 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".