A horizontal partitioning‐based method for frequent pattern mining in transport timetable
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
Abstract Analysing transport timetables is an important task, as it brings the opportunity to discover which routes commonly lead to delays. Frequent pattern mining is a technique used to support such type of discovery. However, functional dependencies are intrinsic properties present in timetables, particularly related to attributes derived from the origin–destination matrix. Such functional dependencies compromise the search for patterns in timetables in both the number of association rules (ARs) generated and the computational cost. Several of these ARs refer to the same information. Redundancy removal techniques can reduce the number of ARs. However, these techniques are designed to be used after mining finishes, which increases the computational cost of finding useful ARs. This work presents timetable pattern mining (T‐mine), a novel method for frequent pattern mining that improves knowledge discovery in timetables. We evaluated T‐mine using Brazilian Flight Data and compared T‐mine with the direct application of frequent pattern mining approaches with and without functional dependencies. Our experiments indicate that T‐mine is about one order magnitude faster than other methods with functional dependencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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