Data clustering using multi-objective hybrid evolutionary algorithm
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
This paper proposes a multi-objective evolution strategy (ES) hybridized with a k-means algorithm to address a data clustering problem whose objective is minimizing both clustering error and cluster number. Contrary to the conventional data clustering problem with a predetermined number of clusters, the bi-objective problem considered in this study has a set of clustering solutions whose cluster numbers are different from one another. This enables to secure the best clustering result that fits specific needs without restricting the cluster number. To find the solution set, the hybrid ES evolves a population of solution candidates each of which represents a variable number of cluster centroids. While evolving the population, special ES operators dedicated to the bi-objective clustering problem are used. Whenever the hybrid ES creates a new set of cluster centroids, it is fine-tuned by the k-means algorithm. The experiment results show that the hybrid ES outperforms the conventional ES and KMA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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