Characterization of Linkage-based Clustering
Bibliographic record
Abstract
Clustering is a central unsupervised learning task with a wide variety of applications. Not surprisingly, there exist many clustering algorithms. However, unlike classification tasks, in clustering, different algorithms may yield dramatically different outputs for the same input sets. A major challenge is to develop tools that may help select the more suitable algorithm for a given clustering task. We propose to address this problem by distilling abstract properties of clustering functions that distinguish between the types of input-output behaviors of different clustering paradigms. In this paper we make a significant step in this direction by providing such property based characterization for the class of linkage based clustering algorithms. Linkage-based clustering is one the most commonly used and widely studied clustering paradigms. It includes popular algorithms like Single Linkage and enjoys simple efficient algorithms. On top of their potential merits for helping users decide when are such algorithms appropriate for their data, our results can be viewed as a convincing proof of concept for the research on taxonomizing clustering paradigms by their abstract properties. 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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".