A High-Performance Distributed Algorithm for Mining Association Rules

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http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250932

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	ASCII Text	x
	Assaf Schuster, Ran Wolff, Dan Trock, "A High-Performance Distributed Algorithm for Mining Association Rules," Data Mining, IEEE International Conference on, pp. 291, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250932, author = {Assaf Schuster and Ran Wolff and Dan Trock}, title = {A High-Performance Distributed Algorithm for Mining Association Rules}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {291}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250932}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - A High-Performance Distributed Algorithm for Mining Association Rules SN - 0-7695-1978-4 SP EP A1 - Assaf Schuster, A1 - Ran Wolff, A1 - Dan Trock, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Assaf Schuster, Technion - Israel Institute of Technology

Ran Wolff, Technion - Israel Institute of Technology

Dan Trock, Technion - Israel Institute of Technology

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speedup with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm.

Citation:

Assaf Schuster, Ran Wolff, Dan Trock, "A High-Performance Distributed Algorithm for Mining Association Rules," icdm, pp.291, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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