摘要
提出了一种基于序列数据挖掘的中文网页候选特征的选择方法,并用于中文网页分类模型.该方法运用改进的PAT树结构挖掘频繁出现在同一类中文网页中的字符串,通过净频率计算,挖掘出中文网页中频繁出现的有意义的词、短语、英文单词等,并结合CHI算法得到文本特征.实验表明,该算法不仅能挖掘出传统方法所选择出的绝大部分特征,还能挖掘出一些有意义的、切词系统词库中没有的、能反映分类特点的人名,地名,新词、常用语、外文单词等.
A method is proposed to select feature candidates.from Chinese websites on the basis of sequential data mining, and it is used in the model of Chinese websites classification. This method uses improved PAT tree data structure to mine the frequent strings in the same class of Chinese websites, calculates the net frequency, mines frequent meaningful words, phrases, and English words from Chinese websites, and obtains text features with the help of the CHI algorithm. Experiments show that this algorithm not only mines most of the features selected by the traditional algorithm, but alse mines some new meaningful personnames, placenames, new words, phrases, and foreign words.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2006年第3期97-100,共4页
Journal of Shandong University(Natural Science)
基金
福建省科技计划资助项目(2004I014)
关键词
序列数据挖掘
PAT树
净频率
频繁字串
中文网页分类
sequential data mining
pat-tree
net frequency
frequent string
chinese web page classification
