DocumentCode :
1626928
Title :
Mining association rules using fast algorithm
Author :
Anandhavalli, M. ; Jain, Sandip ; Chakraborti, Abhirup ; Roy, Nayanjyoti ; Ghose, M.K.
Author_Institution :
Dept. of Comput. Sci. Eng., Sikkim Manipal Inst. of Technol., East Sikkim, India
fYear :
2010
Firstpage :
400
Lastpage :
403
Abstract :
The most time consuming operation in Priori-like algorithms for association rule mining is the computation of the frequency of the occurrences of itemsets (called candidates) in the database. In this paper, a fast algorithm has been proposed for generating frequent itemsets without generating candidate itemsets and association rules with multiple consequents. The proposed algorithm uses Boolean vector with relational AND operation to discover frequent itemsets. Experimental results shows that combining Boolean Vector and relational AND operation results in quickly discovering of frequent itemsets and association rules as compared to general Apriori algorithm.
Keywords :
Boolean algebra; data mining; relational algebra; Boolean vector; association rules mining; database itemsets; fast algorithm; frequent itemsets generation; priori-like algorithms; relational AND operation; Association rules; Computer science; Data engineering; Data mining; Data preprocessing; Frequency; Itemsets; Relational databases; Transaction databases; Association Rule Mining (ARM); Boolean vector; Frequent itemsets; relational AND operation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advance Computing Conference (IACC), 2010 IEEE 2nd International
Conference_Location :
Patiala
Print_ISBN :
978-1-4244-4790-9
Electronic_ISBN :
978-1-4244-4791-6
Type :
conf
DOI :
10.1109/IADCC.2010.5422920
Filename :
5422920
Link To Document :
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