Abstract
XML has been extensively used in many information retrieval related applications. As an important data mining technique, clustering has been used to analyze XML data. The key issue of XML clustering is how to measure the similarity between XML documents. Traditionally, document clustering methods use the content information to measure the document similarity, the structural information contained in XML documents is ignored. In this paper, we propose a model called Structure and Content Vector Model(SCVM) to represent the structure and content information in XML documents. Based on the model, we define similarity measure that can be used to cluster XML documents. Our experimental results show that the proposed model and similarity measure are effective in identifying similar documents when the structure information contained in XML documents is meaningful. This method can be used to improve the precision and efficiency in XML information retrieval.
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Zhang, L., Li, Z., Chen, Q., Li, N. (2010). Structure and Content Similarity for Clustering XML Documents. In: Shen, H.T., et al. Web-Age Information Management. WAIM 2010. Lecture Notes in Computer Science, vol 6185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16720-1_12
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DOI: https://doi.org/10.1007/978-3-642-16720-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16719-5
Online ISBN: 978-3-642-16720-1
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