摘要
由于缺乏类信息,使得无监督文本特征选择问题一直未较好地加以解决。为此,对该问题进行了研究并提出了一个基于论域划分的无监督文本特征选择。该方法主要是把论域划分的思想引入到无监督文本特征选择之中,其首先使用一种新型无监督文档进行文本特征初选以过滤低频的噪声词,然后再使用所给的基于论域划分的属性约简进行文本特征优选。实验结果表明这个方法能够克服文本聚类时缺乏类的先验知识的不足,可以较好地解决无监督文本特征选择问题。
Due to the lack of class labels, unsupervised text feature selection problem hasnt been resolved ef- fectively. Therefore, this problem was studied and an unsupervised text feature selection method based on domain division was proposed. This method mainly makes use of supervised text feature selection doing unsupervised text feature selection. Firstly those low-frequency noise words are flihered by using a new wnsupervised document to make the text characteristics primary election, and then employs a presented attribute reduction based on domain division for text feature optimization. The experimental results show that this method can overcome the clustering flaw which lacks of transcendent knowledge and solve unsut)ervised text feature selection nrob]em well.
出处
《科学技术与工程》
北大核心
2013年第7期1836-1839,共4页
Science Technology and Engineering
基金
国家自然科学基金(61201447)
河南省基础与前沿技术研究计划项目(102300410266
122300410287)
郑州市科技计划项目(121PPTGG362-12)
郑州轻工业学院博士科研基金(2010BSJJ038)资助
关键词
文本聚类
特征选择
文档频
论域划分
text clustering feature selection document frequency domain division