Candidate List Maintenance in High Utility Sequential Pattern Mining
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
High utility sequential pattern mining (HUSPM) lends the aspect of item value or importance to sequential pattern mining by identifying patterns that comprise a significant level of utility in a database. This paper addresses the challenge of establishing upper bounds on future candidate pattern utilities in an effort to reduce the search space required to identify the full set of patterns, and proposes a new approach where a list of possible candidate concatenation items is maintained. This list specifies the only items that ever need to be considered as possible candidates for concatenation with a sequential pattern being considered, or any future sequential pattern appearing as a descendant in the search tree. As a result of the elimination of items that are known to have no possibility of appearing in future high utility sequential patterns, an approach is presented that exploits this knowledge and computes a significantly tighter upper bound on the utilities of the such patterns. Tests on a variety of publicly available datasets show a dramatic reduction in the number of candidates considered, and the time taken to identify the full set of high utility sequential patterns is significantly reduced accordingly.
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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 | 0.000 |
| Open science | 0.001 | 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