PLT- Positional Lexicographic Tree: A New Structure for Mining Frequent Itemsets
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
Association rules have proved their influence in different industrial fields, where their goal is to identify the relations existing among the events that are stored in large databases. However, in order to enumerate the association rules, there is a need to identify the frequent set of itemsets (i.e. those events that occur together in a sufficient number of transactions). In this paper, a new representation structure for the data stored in any transactional database is proposed. This structure, which we refer to as positional lexicographic tree (PLT), provides an efficient mechanism for subset checking based on a summary of the data extracted from the database. This makes PLT a promising tool for most of the existing data mining approaches. Moreover, our proposed PLT structure regulates the data in the database so that they can be applicable to compression and indexing techniques, which makes PLT suitable for supporting large databases. First, we introduce the PLT construction process, then highlight the different mining approaches that can be modulated to take advantage of PLT. We then present our algorithm and finally prove its correctness
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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 | 0.000 | 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