摘要
结合网络舆情分析的应用需求背景,首先介绍了文本信息的处理,然后探讨了文本聚类中的K-means算法,针对其对初始聚类中心的依赖性的特点,对算法加以改进。基于文档标题能够代表文档内容的思想,改进算法采用稀疏特征向量表示文本标题,计算标题间的稀疏相似度,确定初始聚类中心。最后实验证明改进的K-means算法提高了聚类的准确度;与基于最大最小距离原则的初始中心选择算法比较,提高了执行效率,同时保证了聚类准确度。
Combining background application requirement of online public opinion analysis,this paper firstly introduces the processing of text information,and then discusses the K-means algorithm of the text clustering,according to its characteristic that clustering results depend on the centers of initial clustering,and improves it.Based on the thought that text title can express its content,the improved algorithm uses sparse character vector to express text title,calculates the sparse similarity of them and ascertains the centers of initial clustering.The experiments show that the method improves the clustering accuracy.Compared with another algorithm based on the principle of maximum and minimum distance,the improved method heightens the efficiency and ensures the clustering accuracy.
出处
《计算机系统应用》
2011年第3期165-168,196,共5页
Computer Systems & Applications
关键词
网络舆情
K-MEANS算法
文本聚类
稀疏特征向量
online public opinion
K-means clustering algorithm
text clustering
sparse character vector
