Constrained frequent itemset mining from uncertain data streams
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
Frequent itemset mining is a common data mining task for many real-life applications. The mined frequent itemsets can be served as building blocks for various patterns including association rules and frequent sequences. Many existing algorithms mine for frequent itemsets from traditional static transaction databases, in which the contents of each transaction (namely, items) are definitely known and precise. However, there are many situations in which ones are uncertain about the contents of transactions. This calls for the mining of uncertain data. Moreover, there are also situations in which users are interested in only some portions of the mined frequent itemsets (i.e., itemsets satisfying user-specified constraints, which express the user interest). This leads to constrained mining. Furthermore, due to advances in technology, a flood of data can be produced in many situations. This calls for the mining of data streams. To deal with all these situations, we propose tree-based algorithms to efficiently mine streams of uncertain data for frequent itemsets that satisfy user-specified constraints.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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