K均值聚类算法的研究与并行化改进

K均值算法是一种常用的聚类分析方法,广泛应用于图像处理和机器学习等领域。但该算法具有较高的计算复杂度,导致了算法具有较大的局限性。为了提高算法的运行效率,本文在深入分析算法基本原理的基础上,利用CUDA架构提供的强大计算能力对该算法进行了并行化改进。实验结果表明,算法在取不同的聚类数时均取得了较高的加速比。

Research on K-means Clustering Algorithm and Parallel Improved

K-means algorithm is a commonly used method of clustering analysis, which is widely used in the fields such as image processing and machine learning.Because of this algorithm has high computational complexity, this algorithm has great limitations in the fields of high real-time demand.In order to improve the efficiency of this algorithm, we deeply analysis the basic principle of this algorithm, and using powerful computation ability provided by CUDA to realize the algorithm, for improved parallel.The experimental results show that the parallel algorithm when using different clustering number has achieved a higher speedup.