Bibliographic record
Abstract
We propose a kernel k-means based unsupervised clustering algorithm. The kernel k-means approaches require finding not only the Correct Number of Clusters (CNC) but also the optimum kernel function parameters in the clustering procedure. Existing index validation approaches use a criterion different from the k-means criterion to find the optimum CNC and also choose kernel parameter by trial and error. The proposed algorithm denoted by kernel k-Minimum Average Central Error (Kernel k-MACE), estimates the CNC while simultaneously providing the optimum value of the Gaussian kernel parameter. The advantage of the method in theory is in its consistency in using only one criterion for all the three steps of clustering, CNC estimation, and kernel function parameter estimation. A novel cluster initialization technique enables Kernel k-MACE to converge in less iterations compared to the existing approaches. Simulation results illustrate superiority of Kernel K-MACE over multiple state-of-the-art unsupervised clustering methods for both real data sets and self-generated data sets having 10%-50% overlap. The method outperforms the existing methods by not only providing more accurate CNC estimates, but also by providing better clustering results evaluated by Adjusted Random Index (ARI) and Normalized Variation Index (NVI).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".