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<p><b>Abstract</b>—With the standardization of XML as an information exchange language over the net, a huge amount of information is formatted in XML documents. In order to analyze this information efficiently, decomposing the XML documents and storing them in relational tables is a popular practice. However, query processing becomes expensive since, in many cases, an excessive number of joins is required to recover information from the fragmented data. If a collection consists of documents with different structures (for example, they come from different DTDs), mining clusters in the documents could alleviate the fragmentation problem. We propose a hierarchical algorithm (S-GRACE) for clustering XML documents based on structural information in the data. The notion of structure graph (s-graph) is proposed, supporting a computationally efficient distance metric defined between documents and sets of documents. This simple metric yields our new clustering algorithm which is efficient and effective, compared to other approaches based on tree-edit distance. Experiments on real data show that our algorithm can discover clusters not easily identified by manual inspection.</p>
Data mining, clustering, XML, semistructured data, query processing.
David Wai-lok Cheung, Siu-Ming Yiu, Nikos Mamoulis, Wang Lian, "An Efficient and Scalable Algorithm for Clustering XML Documents by Structure", IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 82-96, January 2004, doi:10.1109/TKDE.2004.1264824
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