DocumentCode
2889121
Title
Learning Rules from Large Datasets Using Rough Set and Apriori Algorithm
Author
Guo, Sen ; Wang, Zhi Yan ; Zhang, Yang Qing ; Yan, He Ping
Author_Institution
Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
1178
Lastpage
1183
Abstract
This paper presents a mechanism called R_Apriori for learning rules from large datasets. The existing rough set based methods are not applicable for large data sets for its high time and space complexity. In this paper, large data sets are divided into several parts, in combination with Apriori algorithm, implicated rules are derived in liner relation to size of data set. At last, experiment result proves that this method is prior to existing ones
Keywords
computational complexity; data mining; learning (artificial intelligence); rough set theory; very large databases; Apriori algorithm; large dataset; rough set based method; rule learning; space complexity; time complexity; Algorithm design and analysis; Computer science; Concurrent computing; Cybernetics; Data engineering; Data mining; Database systems; Information systems; Knowledge representation; Machine learning; Machine learning algorithms; Set theory; Space technology; Rough set; apriori; large dataset; rule derivation;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258601
Filename
4028242
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