A Parallel Apriori Algorithm for Frequent Itemsets Mining

Author

Ye, Yanbin ; Chiang, Chia-Chu

Author_Institution

Acxiom Corp., Little Rock, AR

fYear

2006

fDate

9-11 Aug. 2006

Firstpage

87

Lastpage

94

Abstract

Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated. One of the implementations optimizing the data structure with a trie by Bodon catches our attention. The results of the Bodon´s implementation for finding frequent itemsets appear to be faster than the ones by Borgelt and Goethals. In this paper, we revised Bodon´s implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented

Keywords

data mining; parallel algorithms; tree data structures; very large databases; Bodon implementation; association rules; data mining; data structure; frequent itemsets mining; parallel Apriori algorithm; parallel computing; transaction databases; Application software; Association rules; Computer science; Concurrent computing; Data mining; Data structures; Drives; Itemsets; Parallel processing; Transaction databases; Apriori; Association Rules; Data Mining; Frequent Itemsets Mining (FIM); Parallel Computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering Research, Management and Applications, 2006. Fourth International Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7695-2656-X

Type

conf

DOI

10.1109/SERA.2006.6

Filename

1691365