Expressive power of an algebra for data mining
Why this work is in the frame
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Bibliographic record
Abstract
The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can accommodate disparate mining tasks? We address this problem by presenting a database model and an algebra for data mining. The database model is based on the 3W-model introduced by Johnson et al. [2000]. This model relied on black box mining operators. A main contribution of this article is to open up these black boxes, by using generic operators in a data mining algebra. Two key operators in this algebra are regionize , which creates regions (or models) from data tuples, and a restricted form of looping called mining loop . Then the resulting data mining algebra MA is studied and properties concerning expressive power and complexity are established. We present results in three directions: (1) expressiveness of the mining algebra; (2) relations with alternative frameworks, and (3) interactions between regionize and mining loop.
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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 | 0.001 |
| 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