T-Trees, Vertical Partitioning and Distributed Association Rule Mining

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Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Frans Coenen, Paul Leng, Shakil Ahmed, "T-Trees, Vertical Partitioning and Distributed Association Rule Mining," Data Mining, IEEE International Conference on, pp. 513, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250965, author = {Frans Coenen and Paul Leng and Shakil Ahmed}, title = {T-Trees, Vertical Partitioning and Distributed Association Rule Mining}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {513}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250965}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - T-Trees, Vertical Partitioning and Distributed Association Rule Mining SN - 0-7695-1978-4 SP EP A1 - Frans Coenen, A1 - Paul Leng, A1 - Shakil Ahmed, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Frans Coenen, The University of Liverpool, UK

Paul Leng, The University of Liverpool, UK

Shakil Ahmed, The University of Liverpool, UK

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250965

In this paper we consider a technique (DATA-VP) for distributed (and parallel) Association Rule Mining that makes use of a vertical partitioning technique to distribute the input data amongst processors. The proposed vertical partitioning is facilitated by a novel compressed set enumeration tree data structure (the T-tree), and an associated mining algorithm (Apriori-T), that allows for computationally effective distributed/parallel ARM when compared with existing approaches.

Citation:

Frans Coenen, Paul Leng, Shakil Ahmed, "T-Trees, Vertical Partitioning and Distributed Association Rule Mining," icdm, pp.513, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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