Abstract
The similarity evaluation between XML documents is the basis of XML structural mining, and it is a crucial factor of the mining result. After introducing the popular XML tree edit method and frequent pattern method for XML data mining, in this paper, we use 10 basic statistics to describe the structural information of the XML documents, and then using the improved Euclidean distance to evaluate the similarity of XML documents. Moreover, in order to verify the performance of the proposed evaluation method, it is applied to XML documents clustering. The experimental results show that our method is superior to the methods based on edit tree or frequent pattern.
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References
W3C Recommendation, Document Object Model (DOM) Level 3 Core Specification (2004), http://www.w3.org/TR/DOM-Level-3-Core/
Bille, P.: A survey on tree edit distance and related problem. Theoretical Computer Science 337, 217–239 (2005)
Selkow, S.M.: The tree-to-tree edit problem. Information Processing Letter 6, 184–186 (1997)
Chawathe, S.S.: Comparing Hierarchical Data in External Memory. In: Proceedings of the 25th VLDB, pp. 90–101 (1999)
Dalamagas, T., Cheng, T., Winkel, K., Sellis, T.K.: A Methodology for Clustering XML Documents by Structure. Information Systems 31(3), 187–228 (2006)
Nayak, R., Iryadi, W.: XML schema clustering with semantic and hierarchical similarity measures. Knowledge-Based Systems Archive 20(4), 336–349 (2007)
Leung, H.-P., Chung, F.-L., et al.: XML Document clustering using Common XPath. In: Proc. of the Internation Workshop on Challenges in Web Information Retrieval and Integration, pp. 91–96 (2005)
Hwang, J.H., Gu, M.S.: Clustering XML Documents Based on the Weight of Frequent Structures. In: Proc. of the 2007 International Conference on Convergence Information Technology, pp. 845–849 (2007)
Zhang, H., Yuan, X., Yang, N., Liu, Z.: Similarity Computation for XML Documents by XML Element Sequence Patterns. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 227–232. Springer, Heidelberg (2008)
SIGMOD Record Datasets (2007), http://www.sigmod.org/record/xml/
INEX. INitiative for the Evaluation of XML Retrieval (2007), http://inex.is.informatik.uni-duisburg.de/
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Wang, CY., Wu, XJ., Li, J., Ge, Y. (2012). Structural Similarity Evaluation of XML Documents Based on Basic Statistics. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds) Web Information Systems and Mining. WISM 2012. Lecture Notes in Computer Science, vol 7529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33469-6_86
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DOI: https://doi.org/10.1007/978-3-642-33469-6_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33468-9
Online ISBN: 978-3-642-33469-6
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