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| 改进遗传算法的K-均值聚类算法研究 | |
| 引用本文： | 徐家宁,张立文,徐素莉,李进.改进遗传算法的K-均值聚类算法研究[J].微计算机应用,2010,31(4). |
| 作者姓名： | 徐家宁 张立文 徐素莉 李进 |
| 作者单位： | 1. 河南科技大学,校团委,洛阳,471003 2. 河南科技大学,电子信息工程学院,洛阳,471003 3. 太原科技大学,计算机科学与技术学院,太原,030024 |
| 基金项目： | 河南省科技攻关计划项目，河南省教育厅科研项目资助 |
| 摘 要： | 传统的k-均值算法对初始聚类中心的敏感很大,极易陷入局部最优值;利用遗传算法或免疫规划算法解决初始聚类中心是较好的方法,但后期容易出现收敛速度缓慢.为了克服上述缺点,文章将免疫原理的选择操作机制引入遗传算法中,使个体浓度和适应度同时对个体的选择施加影响,以此提出基于改进遗传算法的K-均值聚类算法,该方法利用K-均值算法的高效性和改进遗传算法的全局优化搜索能力,较好地解决了聚类中心优化问题.试验结果表明,本算法能够有效改善聚类质量,并且具有较好的收敛速度. |
| 关 键 词： | 聚类分析 遗传算法 免疫机制 K-均值 个体浓度 |
Research on K-means Clustering Algorithm Based on Improved Genetic Algorithm |
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| XU Jianing,ZHANG Liwen,XU Suli,LI Jin.Research on K-means Clustering Algorithm Based on Improved Genetic Algorithm[J].Microcomputer Applications,2010,31(4). | |
| Authors: | XU Jianing ZHANG Liwen XU Suli LI Jin |
| Affiliation: | XU Jianing2,ZHANG Liwen1,XU Suli1,LI Jin3(1Institute of Electronic , Information,Henan University of Science , Technology,Luoyang Henan,471003,China 2Enrollment , Employment Consultation Section,China 3School of Computer Science , Technology,Taiyuan University of Science ,Technology,Taiyuan Shanxi,030024,China) |
| Abstract: | The traditional K-means algorithm has the shortcoming that plunges into a local optimum prematurely because of sensitive selection of the initial cluster center.Using the genetic or immune algorithm into K-means algorithm to optimize cluster center is much better than using other algorithms,but there appeares the local early phenomenon easily.In order to overcome the shortcomings mentioned above,a K-means clustering algorithm based on improved Genetic Algorithm is proposed,which useing the advantages of imm... |
| Keywords: | clustering analysis genetic algorithm immune principle K-means algorithm individual density |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |