Gauging- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"> <mml:mi>β</mml:mi> </mml:math> : Border-aware hierarchical clustering based on density and proximity
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
Data clustering plays a crucial role in scientific discovery and various real-world applications. However, many clustering algorithms encounter challenges that compromise their effectiveness and the accuracy of data grouping. This paper addresses three key challenges faced by most algorithms: (1) parameter setting, (2) data convexity, and (3) data separation. The proposed algorithm leverages density-based methods to identify and remove border points, effectively separating data sets. A hierarchical, single-linkage-based algorithm is then applied to the remaining points to generate the main clusters. Finally, the border points are reintegrated into the formed clusters. Experimental results demonstrate that the algorithm is capable of handling both convex and non-convex, as well as well-separated and poorly-separated, data sets. The impact of parameter settings on clustering outcomes is thoroughly investigated. Additionally, further experiments on real-world data sets reveal that the consistency of clustering results with classification labels strongly depends on an appropriate measure of sample similarity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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