Bibliographic record
Abstract
Among the studies of KDD, R.Agrawal had presented a theory of association rules based on the basket data, which is the famous algorithm Apriori for data mining. The algorithm is executed in two steps, the large itemsets are generated at first and the set of rules generated afterwards. The algorithms presented by others thereafter still use the ideas of Apriori, that is any subset of a large itemset must also be large. Extending the large ( k-1 ) itemsets L k-1 using JOIN operation generates the set of candidate k itemsets C k . The generation of the large itemsets takes up a large amount of calculation, because scale of database is large and also the C k . The algorithm AprioriTid has set an identifier Tid for each transaction and the database is scanned only once, other scans (e.g. kth scan) are executed at corresponding data set C k . But the efficiency increased by the algorithm AprioriTid is not evidently because the difference of scale of database and data set C k is small. A new algorithm using multi segment for support is presented in this paper. The support of an itemset is divided into a lot of segments, and the counting for the different scale of transactions are recorded in corresponding segments. We can predict whether an itemset may be contained in a large itemset of lager scale, because the algorithm can calculate the multi segment support for the itemsets in one scan. This algorithm not only enhances the information gain ratio in database scanning, but also can find that some supersets are not the members of the large itemsets in advance, and to reduce the size of candidate itemsets by deleting these itemsets. In order to increase the efficiency of generating the large itemsets, the reduction of the scale of data set in each scan is according to the theorem 1 in this paper. A performance comparison of this algorithm and Apriori is given at the end of the paper.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".