Association Mining without Support Thresholds
Bibliographic record
Abstract
We revisit the problem of mining for associations from a register transaction database. To ensure the search for associations remains tractable, solutions have typically made use of a user-specified (data independent) support threshold. We consider applications for which such a support threshold is inappropriate or unavailable. We tackle the intractable problem of finding the most interesting associations without using support to prune the search space. Instead, we propose a novel probabilistic algorithm for finding the most interesting associations without enumerating and counting an exponential number of alternatives. We demonstrate empirically that our (approximate) solution finds, with high probability, the most interesting associations. 1. Contact author; College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332 (404) 894-7008 2. Department of Computer Science, University of Toronto 2 1 Introduction The mining of register transaction databases was one of the f...
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".