Efficient dynamic mining of constrained frequent sets
Bibliographic record
Abstract
Data mining is supposed to be an iterative and exploratory process. In this context, we are working on a project with the overall objective of developing a practical computing environment for the human-centered exploratory mining of frequent sets. One critical component of such an environment is the support for the dynamic mining of constrained frequent sets of items. Constraints enable users to impose a certain focus on the mining process; dynamic means that, in the middle of the computation, users are able to (i) change (such as tighten or relax) the constraints and/or (ii) change the minimum support threshold, thus having a decisive influence on subsequent computations. In a real-life situation, the available buffer space may be limited, thus adding another complication to the problem.In this article, we develop an algorithm, called DCF, for Dynamic Constrained Frequent-set computation . This algorithm is enhanced with a few optimizations, exploiting a lightweight structure called a segment support map . It enables DCF to (i) obtain sharper bounds on the support of sets of items, and to (ii) better exploit properties of constraints. Furthermore, when handling dynamic changes to constraints, DCF relies on the concept of a delta member generating function , which generates precisely the sets of items that satisfy the new but not the old constraints. Our experimental results show the effectiveness of these enhancements.
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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 | 0.002 | 0.021 |
| 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 | 0.001 | 0.003 |
| 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".