Abstract
XML is increasingly important in data exchange and information management. A large amount of efforts have been spent in developing efficient techniques for storing, querying, indexing and accessing XML documents. In this paper we propose a new approach to clustering XML data. In contrast to previous work, which focused on documents defined by different DTDs, the proposed method works for documents with the same DTD. Our approach is to extract features from documents, modeled by ordered labeled trees, and transform the documents to vectors in a high-dimensional Euclidean space based on the occurrences of the features in the documents. We then reduce the dimensionality of the vectors by principal component analysis (PCA) and cluster the vectors in the reduced dimensional space. The PCA enables one to identify vectors with co-occurrent features, thereby enhancing the accuracy of the clustering. Experimental results based on documents obtained from Wisconsin's XML data bank show the effectiveness and good performance of the proposed techniques.
Original language | English |
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Title of host publication | Proceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 |
Editors | T.M. Khoshgoftaar |
Pages | 658-662 |
Number of pages | 5 |
DOIs | |
State | Published - 1 Dec 2004 |
Event | Proceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 - Boca Raton, FL, United States Duration: 15 Nov 2004 → 17 Nov 2004 |
Other
Other | Proceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 |
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Country/Territory | United States |
City | Boca Raton, FL |
Period | 15/11/04 → 17/11/04 |