Enhanced Mining of High Utility Patterns from Streams of Dynamic Profit
Bibliographic record
Abstract
Frequent pattern mining has been extended to the mining of other useful patterns. These include high-utility patterns. Many traditional high-utility mining algorithms focus on algorithmic efficiency when mining high-utility patterns from static databases. These algorithms rely on an assumption that the unit utility for a given item is a constant. However, as we are living in dynamic world where the unit utility (external unit profit) may change over time, such an assumption may not truly reflect reality in the real world. However, to the best of our knowledge, not a lot of works were done on mining dynamic profit from data streams yet. The emergence of big data has led to some performance challenges such that proper big data management techniques are needed for knowledge discovery from dynamic data streams. Traditional static data mining algorithms cannot directly apply to dynamic data. Furthermore, information in the data stream might not be uniformly distributed so it introduces extra challenges to process the data. Using big data stream processing platforms is necessary when mining real-world data stream. Leveraging the big data processing framework requires having scalable algorithms. In this paper, we present an enhanced high-utility data stream algorithm—called EHUI-Stream—to speed up the execution time and reduce memory usage. Utilizing our proposed algorithm, the data stream mining performance is expected to be further enhanced against both real-world datasets and synthetic datasets. Evaluation results on real-life data demonstrate the effectiveness of our platform in scalable high-utility pattern mining for dynamic profit from data streams for social and behavioral analytics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".