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| 基于改进GA的K-均值聚类算法 | |
| 引用本文： | 向永生,张颖,刘燕婷,陈曦. 基于改进GA的K-均值聚类算法[J]. 长沙理工大学学报(自然科学版), 2009, 6(1): 73-76 |
| 作者姓名： | 向永生 张颖 刘燕婷 陈曦 |
| 作者单位： | 长沙理工大学,城南学院,湖南,长沙,410076;长沙理工大学,计算机与通信工程学院,湖南,长沙,410004 |
| 基金项目： | 湖南省自然科学基金，湖南省教育厅科研项目 |
| 摘 要： | 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，但容易出现局部早熟现象．为了克服以上缺点，借助免疫机制的优点，将免疫原理的选择操作机制引入遗传算法中，提出基于改进遗传的K-均值聚类算法，该方法结合K-均值算法的高效性和改进遗传算法的全局优化能力，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． |
| 关 键 词： | 聚类 改进遗传算法 K-均值 |
K-means clustering algorithm based on improved GA |
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| XIANG Yong-sheng,ZHANG Ying,LIU Yan-ting,CHEN Xi. K-means clustering algorithm based on improved GA[J]. Journal of Changsha University of Science and Technology(Natural Science), 2009, 6(1): 73-76 | |
| Authors: | XIANG Yong-sheng ZHANG Ying LIU Yan-ting CHEN Xi |
| Affiliation: | 1.School of Chengnan;Changsha University of Science and Technology;Changsha 410076;China;2.School of Computer and Communication Engineering;Changsha Universityof Science and Technology;Changsha 410004;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 GA is proposed,which uses the advantages of immune idea and intr... |
| Keywords: | clustering improved GA K-means |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |