Research on K-means Clustering Algorithm Based on Improved Genetic 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
The traditional K-means algorithm has the shortcoming that plunges into a local optimum prematurely because of sensitive selection of the initial cluster center.Using the genetic or immune algorithm into K-means algorithm to optimize cluster center is much better than using other algorithms,but there appeares the local early phenomenon easily.In order to overcome the shortcomings mentioned above,a K-means clustering algorithm based on improved Genetic Algorithm is proposed,which useing the advantages of immune idea and introducing the idea of selection opreation of immune principle into Genetic Algorithm,in which the selection of individual was impacted by its density and fitness.The algorithm can solve the problem of optimizing cluster center by combining the high efficiency of K-means algorithm with the ability of global optimization of impoved Genetic Algorithm.The experimental results show that new algorithm has improved the clustering quality effectively,and greater global searching capability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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