LOCAL PECULIARITY ORIENTED DATA MINING AND ITS APPLICATION IN OUTLIER DETECTION
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
Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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