Bibliographic record
Abstract
Knowledge discovery in databases, commonly referred to as data mining, has attracted enormous research efforts from different domains such as databases, statistics, artificial intelligence, data visualization, and so forth in the past decade. Most of the research work in data mining such as clustering, association rules mining, and classification focus on discovering large patterns from databases (Ramaswamy, Rastogi, & Shim, 2000). Yet, it is also important to explore the small patterns in databases that carry valuable information about the interesting abnormalities. Outlier detection is a research problem in small-pattern mining in databases. It aims at finding a specific number of objects that are considerably dissimilar, exceptional, and inconsistent with respect to the majority records in an input database. Numerous research work in outlier detection has been proposed such as the distribution-based methods (Barnett & Lewis, 1994; Hawkins, 1980), the distance-based methods (Angiulli & Pizzuti, 2002; Knorr & Ng, 1998, 1999; Ramaswamy et al.; Wang, Zhang, & Wang, 2005), the density-based methods (Breuning, Kriegel, Ng, & Sander, 2000; Jin, Tung, & Han, 2001; Tang, Chen, Fu, & Cheung, 2002), and the clustering-based methods (Agrawal, Gehrke, Gunopulos, & Raghavan, 1998; Ester, Kriegel, Sander, & Xu, 1996; Hinneburg & Keim, 1998; Ng & Han, 1994; Sheikholeslami, Chatterjee, & Zhang, 1999; J. Zhang, Hsu, & Lee, 2005; T. Zhang, Ramakrishnan, & Livny, 1996).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".