Efficient algorithms for stream mining of constrained frequent patterns in a limited memory environment
Bibliographic record
Abstract
As technology advances, streams of data can be rapidly generated in many real-life applications. This calls for stream mining, which searches for implicit, previously unknown, and potentially useful information---such as frequent patterns---that might be embedded in continuous data streams. However, most of the existing algorithms do not allow users to express the patterns to be mined according to their intentions, via the use of constraints. As a result, these unconstrained mining algorithms can yield numerous patterns that are not interesting to the users. Moreover, many existing tree-based algorithms assume that all the trees constructed during the mining process can fit into memory. While this assumption holds for many situations, there are many other situations in which it does not hold. Hence, in this paper, we develop efficient algorithms for stream mining of constrained frequent patterns in a limited memory environment. Our algorithms allow users to impose a certain focus on the mining process, discover from data streams all those frequent patterns that satisfy the user constraints, and handle situations where the available memory space is limited.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".