Scholarlink

<p>The exponential increase in data has created opportunities and challenges for machine learning, particularly in annotating unlabeled datasets, a task that is time-intensive and often requires domain expertise. Clustering algorithms, such as k-means, offer an alternative approach but face limitations, including susceptibility to local minima. Metaheuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been applied to enhance clustering quality. This study extends prior work by comparing the Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Heap-Based Optimizer (HBO), and Golden Eagle Optimizer (GEO) for clustering problems, focusing on three population initialization strategies: random initialization, K-means initialization, and K-means++ initialization. While random initialization generates clusters randomly, K-means and K-means++ methods integrate centroid spread techniques to enhance optimization. A diverse range of datasets was used, from small, well-known benchmarks to larger, higher-dimensional datasets to evaluate the performance of these approaches against GA, PSO, Differential Evolution (DE), and Artificial Bee Colony (ABC). The results reveal that hybrid methods combining K-means initialization with metaheuristic optimization significantly reduce within-cluster distances, outperforming traditional clustering methods. These findings suggest improved cluster quality, offering potential benefits for downstream machine learning tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing Clustering Performance: A Comparative Analysis of Metaheuristics and Enhanced Initialization Strategies

  • 期刊论文

  • 发表日期：

    2025年9月9日

查看全文
  • Duha Al-Darras,
  • Nesreen A. Hamad,
  • Bashar Al-Shboul

摘要

The exponential increase in data has created opportunities and challenges for machine learning, particularly in annotating unlabeled datasets, a task that is time-intensive and often requires domain expertise. Clustering algorithms, such as k-means, offer an alternative approach but face limitations, including susceptibility to local minima. Metaheuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been applied to enhance clustering quality. This study extends prior work by comparing the Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Heap-Based Optimizer (HBO), and Golden Eagle Optimizer (GEO) for clustering problems, focusing on three population initialization strategies: random initialization, K-means initialization, and K-means++ initialization. While random initialization generates clusters randomly, K-means and K-means++ methods integrate centroid spread techniques to enhance optimization. A diverse range of datasets was used, from small, well-known benchmarks to larger, higher-dimensional datasets to evaluate the performance of these approaches against GA, PSO, Differential Evolution (DE), and Artificial Bee Colony (ABC). The results reveal that hybrid methods combining K-means initialization with metaheuristic optimization significantly reduce within-cluster distances, outperforming traditional clustering methods. These findings suggest improved cluster quality, offering potential benefits for downstream machine learning tasks.