DocumentCode
3195719
Title
Study on the Application of Multi-level Association Rules Based on Granular Computing
Author
Shen, Yanguang ; Shen, Jing ; Fan, Yongjian
Author_Institution
Sch. of Inf. & Electron. Eng., Hebei Univ. of Eng., Handan, China
Volume
3
fYear
2010
fDate
11-12 May 2010
Firstpage
564
Lastpage
567
Abstract
For the issue that classical association rules can not mine multi-level association rules, we proposed a multi-level association rule mining method based on binary information granules in granular computing and multiple minimum supports, and gave the definition of the support and confidence based on binary information granules. In this new association rules method, we can reduce the generation search space of frequent itemsets, extract multi-level association information(including cross-level information), and find more effective rules.
Keywords
artificial intelligence; data mining; association rule mining method; binary information granules; cross-level information; granular computing; multilevel association rules; Agricultural products; Association rules; Automation; Data mining; Databases; Explosions; Frequency; Information processing; Itemsets; data mining; granular computing; multi-level association rule; multiple minimum supports;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location
Changsha
Print_ISBN
978-1-4244-7279-6
Electronic_ISBN
978-1-4244-7280-2
Type
conf
DOI
10.1109/ICICTA.2010.656
Filename
5522880
Link To Document