Fast Algorithms for Frequent Itemset Mining from Uncertain Data
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
The majority of existing data mining algorithms mine frequent item sets from precise databases. A well-known algorithm is FP-growth, which builds a compact FP-tree structure to capture important contents of the database and mines frequent item sets from the FP-tree. However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent item set mining from uncertain databases. UFP-growth is one of the frequently cited algorithms for mining uncertain data. However, the corresponding UFP-tree structure can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of looser upper bounds on expected supports. To solve this problem, we propose two compact tree structures which capture uncertain data with tighter upper bounds than existing tree structures. We also designed two algorithms that mine frequent item sets from our proposed trees. Our experimental results show the tightness of bounds to expected supports provided by these algorithms.
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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 | 0.003 | 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