Title :
An Algorithm of Commodities Association Rules Mining in E-Commerce Based on Rough Set
Author :
Peng, Yun ; Wan, Hongxin
Author_Institution :
Comput. & Inf. Eng. Coll., Jiangxi Normal Univ., Nanchang, China
Abstract :
E-commerce commodities contain a large number of associated information, an algorithm on how to mine association rules based on rough set is proposed in this paper. The different feature vectors extracted from different types of commodities can be looked as a prerequisite for getting association rules. By using the knowledge reduction theory the associated commodities can be drown as a minimum set of commodities and we can get the association rules from the set. We have designed a more efficient algorithm for mining association rules and the algorithm is also described in detail by example.
Keywords :
data mining; electronic commerce; rough set theory; commodities association rules mining; e-commerce; feature vectors; knowledge reduction theory; rough set; Algorithm design and analysis; Association rules; Classification algorithms; Correlation; Electronic commerce; Feature extraction;
Conference_Titel :
Internet Technology and Applications, 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5142-5
Electronic_ISBN :
978-1-4244-5143-2
DOI :
10.1109/ITAPP.2010.5566083