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Published Articles >> Table of Contents >> Abstract
Fourth IEEE International Conference on Data Mining (ICDM'04)
pp. 355-358
Scalable Multi-Relational Association Mining
Amanda Clare, University of Wales Aberystwyth, UK
Hugh E. Williams, RMIT University, Melbourne, Australia
Nicholas Lester, RMIT University, Melbourne, Australia
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10035
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| Abstract |
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We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools.
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 Additional Information
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Citation:
Amanda Clare, Hugh E. Williams, Nicholas Lester,
"Scalable Multi-Relational Association Mining,"
icdm,
pp. 355-358,
Fourth IEEE International Conference on Data Mining (ICDM'04),
2004
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