基于强化学习的聚类算法

Clustering Algorithm Based on Reinforcement Learning

创新性地将强化学习技术引入聚类算法中,旨在解决传统聚类方法面临的两大难题:初始聚类中心选择的不确定性以及计算过程中欧氏距离划分样本导致的高时间复杂度。通过引入强化学习的奖惩机制,设计了一种基于“代理”Agent的行为选择策略,有效替代了传统的欧氏距离计算过程,从而消除了初始聚类中心对算法稳定性的潜在影响,并大幅提升了算法的收敛速度。提出了一种全新的基于强化学习的聚类算法,不仅在数学上严谨证明了其收敛性,而且在实际应用中展现了显著优势。通过数值实验验证,该算法在聚类准确率上较传统方法有明显提升,同时在算法性能上也表现出更加优越的特点,这一研究对于提升数据处理效率和准确性具有重要意义。

Innovatively introducing reinforcement learning techniques into clustering algorithms, this research aims to address two major challenges faced by traditional clustering methods: the uncertainty in selecting initial cluster centers and the high time complexity caused by the Euclidean distance metric in sample classification. By introducing the reward-punishment mechanism of reinforcement learning, this paper designs a behavior selection strategy based on an “agent,” effectively replacing the traditional Euclidean distance calculation process. This approach eliminates the potential impact of initial cluster centers on the stability of the algorithm and significantly improves the convergence speed. A novel clustering algorithm based on reinforcement learning is proposed, which not only rigorously proves its convergence mathematically but also demonstrates significant advantages in practical applications. Through numerical experiments, it is verified that the algorithm achieves significantly higher clustering accuracy compared to traditional methods, while also exhibiting superior algorithm performance. This research is of great significance for improving the efficiency and accuracy of data processing.