|
|||||
|
|
| 基于半监督的K-means聚类改进算法 | |
| 引用本文： | 李小展. 基于半监督的K-means聚类改进算法[J]. 东莞理工学院学报, 2011, 18(1): 29-32 |
| 作者姓名： | 李小展 |
| 作者单位： | 广东工业大学计算机学院,广州,510006 |
| 摘 要： | 针对原始K-means算法的一系列问题,提出一种基于半监督的K-means聚类改进算法,能够自动进行聚类,找出最优K值,并且最大限度地找出孤立点.首先根据样本集自身的特点,按照"类内尽可能相似"原则一步一步形成数据集,然后对数据集进行"去噪"与合并相似簇,最后,利用少量的标记信息指导和修正聚类结果.在UCI的多个数据集... |
| 关 键 词： | 半监督 K-means算法 聚类改进算法 |
Clustering Algorithm Based on Semi-Supervised K-means |
|
| LI Xiao-zhan. Clustering Algorithm Based on Semi-Supervised K-means[J]. Journal of Dongguan Institute of Technology, 2011, 18(1): 29-32 | |
| Authors: | LI Xiao-zhan |
| Affiliation: | LI Xiao-zhan(Faculty of Computer,Guangdong University of Technology,Guangzhou 510006,China) |
| Abstract: | Original k-means algorithm for a range of issues,which is proposed on the basis of semi-supervised k-means Clustering Algorithm,can automatically cluster,finding the optimal k value,and the maximum outliers.First,according to the own characteristics of sample and the principle of category as similar as possible,data set is formed step by step,then denoised or merged into similar clusters,and finally,the resultant clustering is guided and corrected by using a small amount of tag information.Multiple data set... |
| Keywords: | semi-supervised k-means algorithm clustering algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |