LVCluster: Bounded Clustering using Laguerre Voronoi Diagram
Why this work is in the frame
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Bibliographic record
Abstract
Clustering, a fundamental technique in unsupervised learning, identifies similar groups within a dataset. However, clustering algorithms encounter limitations when requiring a predetermined number of clusters/centroids/labels. This paper proposes a novel approach of clustering by integrating concepts from Voronoi diagrams in Laguerre geometry, namely, Laguerre Voronoi Clustering (LVCluster). Laguerre geometry introduces circles by adding radius weight metric to centroids, enabling dynamic exclusion from clustering criteria. Consequently, this approach offers flexibility by necessitating only one hyperparameter, an upper-bound value for the number of circles. LVCluster can be optimized using gradient descent and can be jointly optimized with deep neural network architectures. The experimental results indicated that LVCluster outperforms clustering algorithms when trained individually and jointly with deep neural networks on increased cluster centroids.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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