A Novel Evaluation Criterion for Density Clustering via Circular Information Granules
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
Density clustering is a pivotal algorithm for data clustering and analysis, finding extensive and significant industrial application. There are two key adjustable parameters in density clustering: cluster radius and minimum number of cluster points. At present, the selection of more suitable parameters predominantly depends on statistical methods and analysis, which lacks a precise and effective evaluation criterion. In this paper, a novel evaluation criterion for density clustering via circular information granules is proposed. It constructs circular information granules based on the density clustering results through the principle of justifiable granularity, and then finds the largest sum of volumes of circular information granules. Consequently, it determines the optimal clustering radius and the minimum number of clustering points. Experimental results show that the proposed method provides a more comprehensive evaluation of density clustering results compared to the existing evaluation criterion.
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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 | 0.001 | 0.002 |
| 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