Please use this identifier to cite or link to this item: https://doi.org/10.1109/69.846290
Title: Discovering structural association of semistructured data
Authors: Wang, K. 
Liu, H. 
Issue Date: 2000
Citation: Wang, K., Liu, H. (2000). Discovering structural association of semistructured data. IEEE Transactions on Knowledge and Data Engineering 12 (3) : 353-371. ScholarBank@NUS Repository. https://doi.org/10.1109/69.846290
Abstract: Many semistructured objects are similarly, though not identically, structured. We study the problem of discovering 'typical' substructures of a collection of semistructured objects. The discovered structures can serve the following purposes: 1) the 'table-of-contents' for gaining general information of a source, 2) a road map for browsing and querying information sources, 3) a basis for clustering documents, 4) partial schemas for providing standard database access methods, and 5) user/customer's interests and browsing patterns. The discovery task is impacted by structural features of semistructured data in a nontrivial way and traditional data mining frameworks are inapplicable. We define this discovery problem and propose a solution.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/39087
ISSN: 10414347
DOI: 10.1109/69.846290
Appears in Collections:Staff Publications

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