Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm
Bibliographic record
Abstract
With k-medoids algorithm, it often takes many iterations to cluster a large dataset, that is, the k-medoids algorithm cannot achieve the optimal performance.Based on cluster validity, this paper tries to optimize the clustering performance of k-medoids algorithm, using the purity algorithm.Specifically, the medoids value was determined by the purity value, and cluster validity was measured with the Davies Bouldin Index (DBI) on the Iris Dataset and the Death/Birth Rate Dataset.The results show that the cluster validity of the proposed purity k-medoids algorithm was better than the conventional k-medoids algorithm.The conventional k-medoids converged in an average of 8.7 iterations on the Death Birth Rate Dataset and 13.2 on the Iris Dataset.By contrast, the purity k-medoids algorithm only needed 2 iterations on either dataset.Therefore, the purity k-medoids algorithm can effectively minimize the number of iterations in the clustering of large datasets.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".