A non-group parallel frequent pattern mining algorithm based on conditional patterns
Why this work is in the frame
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Bibliographic record
Abstract
Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it