Scalable Multi-Relational Association Mining

Brighton, United Kingdom November 01-November 04

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http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10035

Fourth IEEE International Conference ...

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	Amanda Clare, Hugh E. Williams, Nicholas Lester, "Scalable Multi-Relational Association Mining," Data Mining, IEEE International Conference on, pp. 355-358, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10035, author = {Amanda Clare and Hugh E. Williams and Nicholas Lester}, title = {Scalable Multi-Relational Association Mining}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {355-358}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10035}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Scalable Multi-Relational Association Mining SN - 0-7695-2142-8 SP355 EP358 A1 - Amanda Clare, A1 - Hugh E. Williams, A1 - Nicholas Lester, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Amanda Clare, University of Wales Aberystwyth, UK

Hugh E. Williams, RMIT University, Melbourne, Australia

Nicholas Lester, RMIT University, Melbourne, Australia

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.

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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