Gauging-: A Non-Parametric Hierarchical Clustering Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The development of a nonparametric and versatile clustering algorithm has been a longstanding challenge in unsupervised learning due to the exploratory nature of the clustering problem. This study presents a novel algorithm, named Gauging-$\delta$δ, which can handle diverse cluster shapes and operate in a nonparametric manner. The algorithm employs a hierarchical merging process that starts from individual data points until no further clusters can be merged. The central component of Gauging-$\delta$δ is the adaptive mergeability function, which progressively determines if two clusters are mergeable considering the perceptual statistics of the clusters and their environment. Empirical evaluations on 105 synthetic datasets demonstrate the superiority of the proposed algorithm, particularly in accurately handling well-separated clusters. Experiments on real-world datasets highlight the impact of selecting appropriate data features and distance metrics on clustering results.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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