Mining uncertain data for constrained frequent sets
Bibliographic record
Abstract
Data mining aims to search for implicit, previously unknown, and potentially useful pieces of information---such as sets of items that are frequently co-occurring together---that are embedded in data. The mined frequent sets can be used in the discovery of correlation or casual relations, analysis of sequences, and formation of association rules. Since its introduction, frequent set mining has been the subject of numerous studies. Most of these studies find all the frequent sets from transaction databases of precise data, in which items within each transaction are definitely known and precise. However, there are many real-life situations in which the user is interested in only some tiny portions of the entire frequent sets, and there are also many situations in which data in the transaction databases are uncertain. This calls for both (i) constrained frequent set mining (which finds frequent sets that satisfy user constraints indicating the user interest) and (ii) frequent set mining from uncertain data. In this paper, we propose a tree-based system that integrates these two kinds of frequent set mining. The resulting mining system avoids candidate generation; it pushes the user constraints inside the mining process, which avoids unnecessary computation. Consequently, the system effectively mines from transaction databases of uncertain data for only those frequent sets satisfying the user-specified constraints.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".