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Sixth IEEE International Conference on Data Mining (ICDM'06)
Multi-Tier Granule Mining for Representations of Multidimensional Association Rules
Hong Kong
December 18-December 22
ISBN: 0-7695-2701-9
ASCII Text
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Yuefeng Li, Wanzhong Yang, Yue Xu, "Multi-Tier Granule Mining for Representations of Multidimensional Association Rules," Data Mining, IEEE International Conference on, pp. 953-958, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDM.2006.113,
author = {Yuefeng Li and Wanzhong Yang and Yue Xu},
title = {Multi-Tier Granule Mining for Representations of Multidimensional Association Rules},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2006},
issn = {1550-4786},
pages = {953-958},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.113},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Multi-Tier Granule Mining for Representations of Multidimensional Association Rules
SN - 1550-4786
SP953
EP958
A1 - Yuefeng Li,
A1 - Wanzhong Yang,
A1 - Yue Xu,
PY - 2006
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Yuefeng Li, Queensland University of Technology, Australia
Wanzhong Yang, Queensland University of Technology, Australia
Yue Xu, Queensland University of Technology, Australia
It is a big challenge to promise the quality of multidimensional association mining. The essential issue is how to represent meaningful multidimensional association rules efficiently. Currently we have not found satisfactory approaches for solving this challenge because of the complicated correlation between attributes. Multi-tier granule mining is an initiative for solving this challenging issue. It divides attributes into some tiers and then compresses the large multidimensional database into granules at each tier. It also builds association mappings to illustrate the correlation between tiers. In this way, the meaningful association rules can be justified according to these association mappings.
Citation:
Yuefeng Li, Wanzhong Yang, Yue Xu, "Multi-Tier Granule Mining for Representations of Multidimensional Association Rules," icdm, pp.953-958, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006
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