Association rule mining for college students’ physical fitness and athletic performance: an empirical analysis based on the Apriori algorithm
Bibliographic record
Abstract
This study utilizes the Apriori algorithm for association rule mining, aiming to deeply explore the intrinsic connection between college students' physical health and sports performance.The relevant definitions of association rule mining and the application process of Apriori algorithm in this study are elaborated in detail, including data preprocessing, frequent item set generation, and association rule extraction.Through empirical analysis, various combinations of physical fitness factors affecting college students' athletic performance and the corresponding association rules are revealed.For example, under the condition of support degree of 0.598 and confidence level of 0.709, when male students' "stiffness upward grade" is passing, their athletic performance is also passing.By mining the correlation rules between college students' sports performance and physical health, it provides scientific basis and targeted suggestions for physical education and students' health management in colleges and universities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".