Discovering frequent patterns guided by an ontology
Bibliographic record
Abstract
The frequent pattern mining generates a huge amount of patterns and therefore requires the establishment of an effective post-treatment to target the most useful. This paper proposes an approach to discover the useful frequent patterns that integrates knowledge described by the expert and represented in an ontology associated with the data. The approach uses the ontology for benefit from more structured information to remove some frequent patterns of the analysis. The experiments realized with our approach give satisfactory results. L'extraction des motifs fréquents en fouille de données génère une quantité énorme de motifs fréquents et requiert par conséquent la mise en place d'un post-traitement efficace afin de cibler les motifs fréquents les plus utiles. Cet article propose une approche de découverte de motifs fréquents utiles qui intègre les connaissances décrites par l'expert et représentées dans une ontologie associée aux données. L'approche utilise l'ontologie pour bénéficier de plus d'informations structurées afin d'éliminer certains motifs fréquents de l'analyse. Les expérimentations réalisées avec notre approche donnent des résultats satisfaisants.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".