XAR-Miner: Efficient association rules mining for XML data

Sheng Zhang, Ji Zhang, Han Liu, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publication14th International World Wide Web Conference, WWW2005
Pages894-895
Number of pages2
DOIs
StatePublished - Dec 1 2005
Event14th International World Wide Web Conference, WWW2005 - Chiba, Japan
Duration: May 10 2005May 14 2005

Publication series

Name14th International World Wide Web Conference, WWW2005

Other

Other14th International World Wide Web Conference, WWW2005
CountryJapan
CityChiba
Period5/10/055/14/05

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Association rule mining
  • Meta-patterns
  • XML data

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  • Cite this

    Zhang, S., Zhang, J., Liu, H., & Wang, W. (2005). XAR-Miner: Efficient association rules mining for XML data. In 14th International World Wide Web Conference, WWW2005 (pp. 894-895). (14th International World Wide Web Conference, WWW2005). https://doi.org/10.1145/1062745.1062785