A framework for efficent association rule mining in XML data
Article
Article Title | A framework for efficent association rule mining in XML data |
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ERA Journal ID | 17988 |
Article Category | Article |
Authors | Zhang, Ji (Author), Liu, Han (Author), Ling, Tok Wang (Author), Bruckner, Robert (Author) and Tjoa, A. Min (Author) |
Journal Title | Journal of Database Management |
Journal Citation | 17 (3), pp. 19-40 |
Number of Pages | 22 |
Year | 2006 |
Place of Publication | Hershey, PA, USA |
ISSN | 1063-8016 |
1533-8010 | |
Abstract | [Abstract]: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed XML Tree (IX-tree) or Multi-relational Databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized |
Keywords | association rule mining, XML data, data transformation and indexing, concept generalization, meta patterns |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Toronto, Canada |
Carnegie Mellon University, United States | |
National University of Singapore | |
Microsoft, United States | |
Vienna University of Technology, Austria |
https://research.usq.edu.au/item/9z284/a-framework-for-efficent-association-rule-mining-in-xml-data
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