An Efficient Approach for Mining Temporal Fuzzy High-Utility Itemsets
Bibliographic record
Abstract
The motivation behind fuzzy logic in data mining is to address the inherent uncertainty and imprecision in real-world data and make the mined results more interpretable for humans. Temporal Fuzzy High Utility Itemset Mining, which incorporates transaction time, is an emerging field with significant potential for analyzing time-sensitive data. Although several studies in this area have been conducted, for instance, recently fuzzy list-based approaches, a significant challenge remains in joining operations of conditional fuzzy lists when generating candidate itemsets. To solve this, we have proposed a pruning strategy based on item co-occurrences to reduce the number of join operations using anti-monotonic property. Experiments on real datasets show our approach outperforms traditional algorithms in terms of runtime and candidate generations with little memory overhead, up to 95% of non-promising candidates are pruned.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".