Title :
Mining Short Association Rules from Large Database
Author :
Ye, Feiyue ; Chen, Mingxia ; Qian, Jin
Author_Institution :
Coll. of Comput. Sci. & Eng., Jiangsu Teachers Univ. of Technol., Changzhou, China
Abstract :
Discovering association rules from existing large databases is an important technique. In this paper, we propose an effective algorithm for mining short association rules on large database. It is experimentally demonstrated presented algorithm has an advantage over existing algorithm for mining association rule, it has better performance and flexibility. By verifying the real transaction data from a supermarket, the short for mining association rules is effective too.
Keywords :
data mining; database management systems; large database; short association rule mining; transaction data verification; Association rules; Computer science; Data engineering; Data mining; Educational institutions; Electronic mail; Information processing; Itemsets; Test pattern generators; Transaction databases; association rule; data mining; frequent pattern;
Conference_Titel :
Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-0-7695-3699-6
DOI :
10.1109/APCIP.2009.98