Title :
A Parallel Apriori Algorithm for Frequent Itemsets Mining
Author :
Ye, Yanbin ; Chiang, Chia-Chu
Author_Institution :
Acxiom Corp., Little Rock, AR
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;
Conference_Titel :
Software Engineering Research, Management and Applications, 2006. Fourth International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7695-2656-X
DOI :
10.1109/SERA.2006.6
