Clustering XML Documents Based on Structural Similarity

  • Guangming Xing
  • Zhonghang Xia
  • Jinhua Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4443)

Abstract

In this paper, we present a framework for clustering XML documents based on structural similarity between XML documents. Firstly, the validity of using the edit distance between XML documents and schemata as the structural similarity is presented. Secondly, a novel solution is given for schema extraction. The solution is based on the minimum length description (MLD) principle, and allows tradeoff between the schema simplicity and precision based on the user’s specification. Thirdly, clustering XML documents based on the edit distance is discussed. The efficacy and efficiency of our methodology have been tested using both real and synthesized data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Suzuki, N.: Finding an Optimum Edit Script between an XML Document and a DTD. In: ACM SAC’05, Santa Fe, NM, March 2005, pp. 647–653 (2005)Google Scholar
  2. 2.
    Xing, G.: Fast Approximate Matching Between XML Documents and Schemata. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 425–436. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Dalamagas, T., Cheng, T., Winkel, K., Sellis, T.: A methodology for clustering XML documents by structure. Information Systems 31(3), 187–228 (2006)CrossRefGoogle Scholar
  4. 4.
    Thompson, K.: Regular Expression Search Algorithm. Communications of ACM 11(6), 419–422 (1968)MATHCrossRefGoogle Scholar
  5. 5.
    Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading (1974)MATHGoogle Scholar
  6. 6.
    Shasha, D., Zhang, K.: Approximate Tree Pattern Matching. In: Apostolico, A., Galil, Z. (eds.) Pattern Matching Algorithms, Oxford University Press, Oxford (June 1997)Google Scholar
  7. 7.
    Murata, M.: Hedge Automata: A Formal Model for XML Schemata, http://www.xml.gr.jp/relax/hedge_nice.html
  8. 8.
    Nierman, A., Jagadish, H.V.: Evaluating structural similarity in XML documents. In: WebDB 2002, Madison, Wisconsin (June 2002)Google Scholar
  9. 9.
    XML Document Mining Challenge, http://xmlmining.lip6.fr/
  10. 10.
    Chidlovskii, B.: Schema Extraction from XML Data: A Grammatical Inference Approach. In: KRDB’01 Workshop, Rome, Italy, September 15 (2001)Google Scholar
  11. 11.
    Garofalakis, M., Gionis, A., Rastogi, R., Seshadri, S., Shim, K.: Xtract: A System for Extracting Document Type Descriptors from XML Documents. In: SIGMOD Conference, Dallas, Texas, USA, May 16-18, 2000, pp. 165–176 (2000)Google Scholar
  12. 12.
    Karypis, G.: CLUTO A clustering toolkit. Technical Report 02-017, University of Minnesota, Department of Computer Science, Minneapolis (Aug. 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Guangming Xing
    • 1
  • Zhonghang Xia
    • 1
  • Jinhua Guo
    • 2
  1. 1.Department of Computer Science, Western Kentucky University, Bowling Green, KY 42104 
  2. 2.Computer and Information Science Department, University of Michigan - Dearborn, Dearborn, MI 48128 

Personalised recommendations