Title :
Value reduction in rough sets based on Apriori algorithm
Author :
Ma, Yuliang ; Luo, Zhizeng
Author_Institution :
Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou, China
Abstract :
Aiming at value reduction, a kind of RSVR algorithm was presented based on support in association rules via Apriori algorithm. A more effective reduction table can be obtained by deleting those rules with less support according to least support - minsup. The reduction feasibility of this algorithm was achieved by reducing the given decision table. Testing by UCI machine learning database and comparing this algorithm with least value reduction algorithm indicate the validity of RSVR algorithm.
Keywords :
decision tables; learning (artificial intelligence); rough set theory; UCI machine learning database; apriori algorithm; association rules; decision table; least value reduction algorithm; reduction table; rough sets; Association rules; Automation; Data analysis; Data mining; Databases; Machine learning; Machine learning algorithms; Partitioning algorithms; Rough sets; Testing; Apriori algorithm; Association rules; Rough sets; Support; Value reduction;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5499015
