Enhancing K-Means Clustering with Post-Redistribution
Bibliographic record
Abstract
Traditional K-means clustering may converge to suboptimal solutions due to local optima, impacting cluster balance and compactness.To fix this, we suggest an enhanced K-means algorithm that includes a new step for redistribution post-clustering that is based on the sum of squares errors (SSE) and diameter.Our approach introduces a redistribution step focusing on achieving balanced population distribution within clusters.Evaluation metrics include Davies-Bouldin Index (DBI) and Gini coefficient, quantifying improvements in cluster compactness and balance.We compare our method against traditional K-means on diverse datasets, such that a lower value indicates better clustering results.The post-clustering redistribution significantly reduces DBI and Gini coefficient, indicating enhanced cluster quality and balance.This improvement is consistent across various datasets, showcasing the method's reliability and generalizability.Our improved K-means algorithm achieves better cluster balance and compactness by redistributing post-clustering, which also reduces problems with local optima.The method's applicability extends to diverse domains, providing more reliable clustering outcomes with practical implications in areas such as customer segmentation, anomaly detection, pattern recognition, and resource optimization.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".