用全局优化提升K均值聚类效率，适合处理大数据。
Boosting K-means for Big Data by Fusing Data Streaming with Global Optimization
- 结合数据流与变邻域搜索，改进传统局部搜索
- 在多个真实数据集上显著提升聚类准确率与速度
- 适合大规模数据聚类，性能超越现有方法
K均值聚类是数据挖掘的核心方法，但在处理大规模数据时效率下降。为此，我们提出一种新启发式算法，利用变邻域搜索（VNS）元启发式方法优化大数据环境下的K均值聚类。该方法基于从原始大数据集中随机采样得到的部分目标函数景观，对最小平方和聚类（MSSC）公式进行序列化优化。在每个景观中，通过重新初始化所有退化解及部分额外中心点，系统性地探索当前最优解（当前最佳解）的扩展邻域。在大量真实数据集上的广泛且严谨实验表明，将传统局部搜索转化为全局搜索后，该算法显著提升了K均值聚类在大数据环境中的准确率与效率，成为该领域的最新技术标杆。
原文摘要 · Abstract (English)
K-means clustering is a cornerstone of data mining, but its efficiency deteriorates when confronted with massive datasets. To address this limitation, we propose a novel heuristic algorithm that leverages the Variable Neighborhood Search (VNS) metaheuristic to optimize K-means clustering for big data. Our approach is based on the sequential optimization of the partial objective function landscapes obtained by restricting the Minimum Sum-of-Squares Clustering (MSSC) formulation to random samples from the original big dataset. Within each landscape, systematically expanding neighborhoods of the currently best (incumbent) solution are explored by reinitializing all degenerate and a varying number of additional centroids. Extensive and rigorous experimentation on a large number of real-world datasets reveals that by transforming the traditional local search into a global one, our algorithm significantly enhances the accuracy and efficiency of K-means clustering in big data environments, becoming the new state of the art in the field.
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