| 基于粒子群优化的高斯核函数聚类算法 |
| |
| 引用本文： | 于进,钱锋.基于粒子群优化的高斯核函数聚类算法[J].计算机工程,2010,36(14):22-23. |
| |
| 作者姓名： | 于进 钱锋 |
| |
| 作者单位： | 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237 |
| |
| 基金项目： | 国家"863"计划基金，国家自然科学基金资助面上项目，国家杰出青年科学基金，长江学者和创新团队发展计划基金，高等学校学科创新引智计划基金，上海市基础研究基金资助重点项目，上海市重点学科建设基金 |
| |
| 摘 要： | 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。
|
| 关 键 词： | 聚类 粒子群优化 高斯核函数 视频帧数据 |
Gauss Kernel Function Clustering Algorithm Based on Particle Swarm Optimization |
| |
| YU Jin,QIAN Feng.Gauss Kernel Function Clustering Algorithm Based on Particle Swarm Optimization[J].Computer Engineering,2010,36(14):22-23. |
| |
| Authors: | YU Jin QIAN Feng |
| |
| Affiliation: | (Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education,East China University of Science and Technology, Shanghai 200237) |
| |
| Abstract: | 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. |
| |
| Keywords: | clustering Particle Swarm Optimization(PSO) Gauss kernel function video frequency frame data |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程》浏览原始摘要信息 |
|
点击此处可从《计算机工程》下载全文 |
