AN ONLINE FREQUENCY RATE BASED ALGORITHM FOR MINING FREQUENT SEQUENCES IN EVOLVING DATA STREAMS
Why this work is in the frame
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Bibliographic record
Abstract
AbstractMining sequential patterns for discovering frequent sequences has been widely studied as a data mining problem. A challenging research is to extend its use to data streams. A data steam is an unbounded, continuously generated sequence of data transactions. In this paper, we propose an online single-pass algorithm called OFSD (Online Frequent Sequence Discovery), to mine the set of all frequent sequences in a data stream whose frequency rates satisfy a minimum user defined frequency rate (fu). The algorithm significantly reduces the number of elements in the candidate set (a set of candidate sequences that should be kept for further exploration) that efficiently increases its performance in comparison with other general solutions. The simulation results show the effects of fu variation and the application defined threshold (CM) on the frequent phrase detection process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.000 |
| 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