Abstract
A semi-structured document has more structured information compared to an ordinary document, and the relation among semi-structured documents can be fully utilized. In order to take advantage of the structure and link information in a semi-structured document for better mining, a structured link vector model (SLVM) is presented in this paper, where a vector represents, a document, and vectors’ elements are determined by terms, document structure and neighboring documents. Text mining based on SLVM is described in the procedure of K-means for briefness and clarity: calculating document similarity and calculating cluster center. The clustering based on SLVM performs significantly better than that based on a conventional vector space model in the experiments, and its F value increases from 0.65–0.73 to 0.82–0.86.
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This research is supported by National Technology Innovation Project and Peking University Graduate Student Development Foundation as one of doctoral dissertation’s innovative research.
YANG Janwu is a Ph.D. candidate in the Institute of Computer Science and Technology, Peking University, China, where he received the M.S. degree in 1999. His current research interests include SGML/XML and data mining.
CHEN Xiaoou obtained his B.S. degree from the Department of Computer Science and Technology, National Defense University in 1983. He has been a research staff member at the Institute of Computer Science and Technology, Peking University since 1990, and has been a professor, since 2000. He is the president of Founder Research and Development Center. His current research interests are image processing, XML data exchange and representation.
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Yang, J., Chen, X. A semi-structured document model for text mining. J. Comput. Sci. & Technol. 17, 603–610 (2002). https://doi.org/10.1007/BF02948828
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DOI: https://doi.org/10.1007/BF02948828