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]: 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)|
|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|
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