A Synthetic Data Generator for Clustering and Outlier Analysis
Bibliographic record
Abstract
Abstract. We present a distribution-based and transformation-based approach to synthetic data generation and demonstrate that the approach is very efficient in generating different types of multi-dimensional numerical datasets for data clustering and outlier analysis. We developed a data generating system that is able to systematically create testing datasets based on user’s requirements such as the number of points, the number of clusters, the size, shapes and locations of clusters, and the density level of either cluster data or noise/outliers in a dataset. Two standard probability distributions are considered in data generation. One is uniform distribution and the other is normal distribution. Since outlier detection, especially local outlier detection, is conducted in the context of clusters of a dataset, our synthetic data generator is suitable for both clustering and outlier analysis. In addition, the data format has been carefully designed so that generated data can be visualized not only by our system but also by some popular statistical rendering tools such as statCrunch [16] and statPoint [17] that display data with standard statistical graphical approaches. To our knowledge, our system is probably the first synthetic data generation system that systematically generates datasets for evaluating the clustering and outlier analysis algorithms. Being an object-oriented system, the current data generator can be easily integrated into other data analysis systems. 1
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".