| 摘要: |
| 在文本聚类/分类处理中,一个重要步骤就是寻找文本的合理表示.在被广泛采用的向量空间模型中,一个文本被表示成一个向量,向量的各维是特征项,而向量空间模型的核心问题就是如何进行特征的抽取和选择.在特征的权重计算中,存在一种对偶性现象.利用迭代的方法来处理和利用这种对偶性,获得了文本的隐含概念.实验结果表明,采用概念空间代替原始词空间来表示文本,能够得到更好的聚类结果. |
| 关键词: 文本聚类 向量空间模型 特征抽取 对偶性 隐含概念空间 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(69773008 |
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| The Duplex Strategy of Term Weighting in Text Clustering |
|
BU Dong-bo,BAI Shuo,LI Guo-jie
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| Abstract: |
| An important step in text mining is to find a reasonable representation of the text. In the popular VSM (vector space module), where a text is represented as a vector, the coral problem is to term extraction, selection and weighting. An iteration method is proposed to deal with the duplex phenomena found in term weighting and compute out the latent concept. Experimental results show that the latent concept could help to get better clustering results. |
| Key words: text clustering vector space module term extraction duplex latent concept |
