Sciweavers

Share
ADMA
2008
Springer

MPSQAR: Mining Quantitative Association Rules Preserving Semantics

9 years 5 months ago
MPSQAR: Mining Quantitative Association Rules Preserving Semantics
To avoid the loss of semantic information due to the partition of quantitative values, this paper proposes a novel algorithm, called MPSQAR, to handle the quantitative association rules mining. And the main contributions include: (1) propose a new method to normalize the quantitative values; (2) assign a weight for each attribute to reflect the values distribution; (3) extend the weight-based association model to tackle the quantitative values in association rules without partition; (4) propose a uniform method to mine the traditional binary association rules and quantitative association rules; (5) show the effectiveness and scalability of new method by experiments.
Chunqiu Zeng, Jie Zuo, Chuan Li, Kaikuo Xu, Shengq
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where ADMA
Authors Chunqiu Zeng, Jie Zuo, Chuan Li, Kaikuo Xu, Shengqiao Ni, Liang Tang, Yue Zhang, Shaojie Qiao
Comments (0)
books