MPSQAR: Mining Quantitative Association Rules Preserving Semantics

Authors:
Chunqiu Zeng;Jie Zuo;Chuan Li;Kaikuo Xu;Shengqiao Ni;Liang Tang;Yue Zhang;Shaojie Qiao
Affiliations:
Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065;Computer School of Sichuan University, Chengdu, China 610065
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

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.