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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258601
