An Approach of Improved Traversal Merging of Transaction Data for Faster Apriori Algorithm
Bibliographic record
Abstract
In order to improve the operational efficiency of the Apriori algorithm in the data preprocessing stage of large-scale data and achieve overall optimization of the Apriori project, a fast traversal merge pre-processing method is proposed by integrating an adaptive association mining threshold determination method. Firstly, the proposed fast traversal merging method is analyzed and compared with two benchmark algorithms, and the experimental results show that the running time of the fast traversal merging method is much lower than that of the two benchmark methods; secondly, according to the central limit theorem, a data adaptive support threshold setting method is proposed, which can avoid the subjectivity of the minimum support threshold setting in association mining; finally, the two proposed algorithms are applied to Apriori and the results show that the application of the proposed improved method for association mining gives significantly better results than association mining under the better processing of the benchmark algorithm, and thus can significantly improve the efficiency of solving the shopping basket problem.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".