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| text classification using semi-supervised clustering | |
| Zhang Wen; Yoshida Taketoshi; Tang Xijin | |
| 2009 | |
| Conference Name | 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009 |
| Source | 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009 |
| Conference Date | 37461 |
| Conference Place | Beijing, China |
| Indexed Type | EI |
| Publish Place | United States |
| ISBN | 9780769537054 |
| Department | (1) School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Ashahidai, Tatsunokuchi, Ishikawa 923-1292, Japan; (2) Lab. for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (3) Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China |
| English Abstract | In this paper, mixture models are used to classify documents. The basic assumption for the documents in a collection is that each class is composed of a number of mixture components. By indentifying the components in the document collection, the classes of documents can thereby be identified from each other. A semi-supervised clustering method is proposed to identify the components (clusters), and further, unlabeled data is used to produce more accurate clusters in document collection to correspond the components of document classes. Experimental results show that the proposed method produces better performances than support vector machine (SVM) with linear kernel, and produces comparable performance with Bayesian classifier with Expectation Maximization (EM) in text classification. © 2009 IEEE. |
| Keyword | Classification (Of Information) Maximum Principle Optimization Support Vector Machines |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/8520 |
| Collection | 互联网软件技术实验室 |
| Recommended Citation GB/T 7714 | Zhang Wen,Yoshida Taketoshi,Tang Xijin. text classification using semi-supervised clustering[C]. United States,2009. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2009-张文-BIFE-Text Cl（268KB） | 开放获取 | -- | Application Full Text | |||
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