Exploring Swarm Intelligence for Enhanced Clustering in Artificial Intelligence: A PSO-Kmeans Hybrid Approach
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, the integration of swarm intelligence-based metaheuristic optimization techniques into Artificial Intelligence (AI) has garnered significant attention.This project aims to investigate the potential applications of swarm intelligence techniques within the domain of AI.By leveraging the collective behavior and adaptive nature of swarm intelligence, these metaheuristic optimization methods offer unique opportunities for solving complex problems in AI.Numerous optimization methods have been proposed in academic research to address clustering-related challenges, but swarm intelligence has established a prominent position in the field.Particle swarm optimization (PSO) is the most popular swarm intelligence technique and one of the researchers' favorite areas.In this study, we introduce a novel clustering approach that integrates PSO with the K-means algorithm, aimed at enhancing clustering outcomes by effectively addressing common clustering challenges.The PSO algorithm has been shown to converge successfully during the initial stages of a global search, but around the global optimum.The proposed algorithm is designed to organize a given dataset into multiple clusters.To assess its effectiveness, we tested the algorithm on five different datasets.We then compared its clustering performance with that of the K-means and PSO algorithms, evaluating it based on metrics such as execution time, accuracy, quantization error, and both intra-cluster and inter-cluster distances.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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