XAR-Miner: efficient association rules mining for XML data

Zhang, Sheng and Zhang, Ji and Liu, Han and Wang, Wei (2005) XAR-Miner: efficient association rules mining for XML data. In: 14th International World Wide Web Conference (WWW'05), 10-14 May 2005, Chiba, Japan.

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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 Content 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. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author version deposited in accordance with the copyright policy of the publisher. 'Copyright is held by the author/owner(s).'
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 08 Sep 2009 04:53
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: association rule mining, XML data, meta-patterns
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
URI: http://eprints.usq.edu.au/id/eprint/5636

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