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 :
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