Classification of XML documents

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A. and Hassler, M.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4221263

Journal: CIDM

Pages: 390-396

Publisher: IEEE

This data was imported from Scopus:

Authors: Bouchachia, A. and Hassler, M.

Journal: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Pages: 390-396

ISBN: 9781424407057

DOI: 10.1109/CIDM.2007.368901

With the explosion of XML-based online documents, the task of knowledge discovery from the web becomes highly significant. As an appropriate machinery, classification allows to categorize documents to facilitate that task. A classification approach is introduced in this paper. It is based on the k-nearest neighborhood algorithm that relies on an edit distance measure. The originality of the work lies in combining both the content and the structure of XML documents to compute the edit distance. The approach is empirically evaluated using real-world XML collections. © 2007 IEEE.

This data was imported from Web of Science (Lite):

Authors: Bouchachia, A., Hassler, M. and IEEE

Journal: 2007 IEEE Symposium on Computational Intelligence and Data Mining, Vols 1 and 2

Pages: 390-396

ISBN: 978-1-4244-0705-7

DOI: 10.1109/CIDM.2007.368901

The data on this page was last updated at 04:54 on April 18, 2019.