XAR-miner: efficient association rules mining for XML data

Authors:
Sheng Zhang;Ji Zhang;Han Liu;Wei Wang
Affiliations:
Nanjing Normal University Nanjing, China;University of Toronto, Toronto, Canada;University of Toronto, Toronto, Canada;Nanjing Normal University, Nanjing, China
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

Citing 3
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APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
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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.