NUCLEAR: AN EFFICIENT METHOD FOR MINING FREQUENT ITEMSETS BASED ON KERNELS AND EXTENDABLE SETS
Bibliographic record
Abstract
Frequent itemset (FI) mining is an interesting data mining task. Directly mining the FIs from data often requires lots of time and memory, and should be avoided in many cases. A more preferred approach is to mine only the frequent closed itemsets (FCIs) first and then extract the FIs for each FCI because the number of FCIs is usually much less than that of the FIs. However, some algorithms require the generators for each FCI to extract the FIs, leading to an extra cost. In this paper, based on the concepts of "kernel set" and "extendable set", we introduce the NUCLEAR algorithm which easily and quickly induces the FIs from the lattice of FCIs without the need of the generators. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the FCIs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".