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Clustering DTDs: An interactive two-level approach

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Abstract

XML (eXtensible Markup Language) is a standard which is widely applied in data representation and data exchange. However, as an important concept of XML, DTD (Document Type Definition) is not taken full advantage in current applications. In this paper, a new method for clustering DTDs is presented, and it can be used in XML document clustering. The two-level method clusters the elements in DTDs and clusters DTDs separately. Element clustering forms the first level and provides element clusters, which are the generalization of relevant elements. DTD clustering utilizes the generalized information and forms the second level in the whole clustering process. The two-level method has the following advantages: 1) It takes into consideration both the content and the structure within DTDs; 2) The generalized information about elements is more useful than the separated words in the vector model; 3) The two-level method facilitates the searching of outliers. The experiments show that this method is able to categorize the relevant DTDs effectively.

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Authors and Affiliations

  1. Department of Computer Science, Laboratory for Intelligent Information Processing, Fudan University, 200433, Shanghai, P.R. China

    Zhou Aoying, Qian Weining, Qian Hailei, Zhang Long & Liang Yuqi

  2. Department of Computer Science, Simon Fraser University, Canada

    Jin Wen

Authors
  1. Zhou Aoying
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  2. Qian Weining
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  3. Qian Hailei
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  4. Zhang Long
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  5. Liang Yuqi
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  6. Jin Wen
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Additional information

This work is supported by the NKBRSF of China (Grant No.G1998030414), the National Natural Science Foundation of China (Grant No.60003016), the National Doctoral Research Foundation of China, and the Joint Project with IBM China Research Lab.

The second author is partially supported by Microsoft Research Fellowship.

Zhou Aoying received his M.S. degree in computer science from Sichuan University in 1988, and his Ph.D. degree in computer software from Fudan University in 1993. He is currently a professor in the Department of Computer Science, Fudan University. His main research interests include object-oriented data models for multimedia information, Web/XML data management, data mining and data warehousing, peerto-peer computing, the novel database technologies and their applications to digital library and electronic commerce.

QIAN Weining is a Ph.D. candidate in the Department of Computer Science, Fudan University. His speciality is database and knowledge base. His research interests include clustering, data mining and Web mining.

QIAN Hailei is a graduate student in the Department of Computer Science, Fudan University. She majors in database and knowledge base. Her research interests include clustering and data mining.

ZHANG Long is a graduate student in the Department of Computer Science, Fudan University. He majors in database and knowledge base. His research interests is XML data management.

LIANG Yuqi is a graduate student in the Department of Computer Science, Fudan University. He majors in database and knowledge base. His research interest is XML data management and Web services.

JIN Wen is a Ph.D. candidate in the School of Computing, Simon Fraser University, Canada, supervised by Dr. Jiawei Han. His current research interests are database and data warehousing, data mining, Web mining and XML.

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Zhou, A., Qian, W., Qian, H. et al. Clustering DTDs: An interactive two-level approach. J. Compt. Sci. & Technol. 17, 807–819 (2002). https://doi.org/10.1007/BF02960771

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  • DOI: https://doi.org/10.1007/BF02960771

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