Beyond intratransaction association analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we extend the scope of mining association rules from traditional single-dimensional intratransaction associations, to multidimensional intertransaction associations. Intratransaction associations are the associations among items with the same transaction , where the notion of the transaction could be the items bought by the same customer , the events happened on the same day , and so on. However, an intertransaction association describes the association relationships among different transactions , such as “if(company) A's stock goes up on day 1, B's stock will go down on day 2, but go up on day 4.” In this case, whether we treat company or day as the unit of transaction, the associated items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple contextual properties in the same rule, so that multidimensional intertransaction associations can be defined and discovered. A two-dimensional intertransaction association rule example is “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away,” which involves two dimensions: time and space . Mining intertransaction associations poses more challenges on efficient processing than mining intratransaction associations. Interestingly, intratransaction association can be treated as a special case of intertransaction association from both a conceptual and algorithmic point of view. In this study, we introduce the notion of multidimensional intertransaction association rules, study their measurements— support and confidence—and develop algorithms for mining intertransaction associations by extension of Apriori. We overview our experience using the algorithms on both real-life and synthetic data sets. Further extensions of multidimensional intertransaction association rules and potential applications are also discussed.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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