基于粒子群优化的高斯核函数聚类算法

Gauss Kernel Function Clustering Algorithm Based on Particle Swarm Optimization

针对视频帧数据在时间轴上的高斯分布特征,提出基于样本和高斯核相似性度量的聚类算法,采用度量方法考虑概率分布密度因素,同时利用改进的粒子群优化算法加速聚类过程。实验结果表明,与基于C均值聚类算法相比,该算法具有较强的全局搜索能力和聚类精度,在视频数据聚类分析中具有更高的效率和更佳的聚类效果。

In view of video frequency frame data in time axis which has Gaussian distribution characteristic,a clustering algorithm based on the measure of Gauss kernel function similarity and an Improved Particle Swarm Optimization（IPSO） is presented.The proposed algorithm can realize accuracy clustering by Gauss kernel function similarity measure,and speed up the clustering process by the IPSO.Experimental results show that the proposed algorithm has greater searching capability and clustering accuracy,which is superior to the C-Mean in analysis of video frequency frame data clustering.