DocumentCode
2328922
Title
Parallel algorithm for mining frequent itemsets
Author
Ruan, You-Lin ; Liu, Gan ; Li, Qing-Hua
Author_Institution
Sch. of Inf. Eng., Wuhan Univ. of Technol., China
Volume
4
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
2118
Abstract
Parallel mining frequent itemsets is a key issue in data mining research. A parallel mining algorithm PMFI in distributed database is proposed in this paper, which attempts to make each processor to do independently and decrease the number of candidate of global frequent itemsets according to the relation between local frequent itemsets and global frequent itemsets. Thus, PMFI uses far less communication overhead and fewer synchronization steps, improves efficiency of mining global frequent itemsets.
Keywords
data mining; distributed databases; parallel algorithms; synchronisation; data mining; distributed database; parallel frequent itemset mining; synchronization; Data engineering; Data mining; Distributed databases; Frequency synchronization; Gallium nitride; Itemsets; Iterative algorithms; Machine learning algorithms; Parallel algorithms; Transaction databases; Global Frequent Itemsets; Local Frequent Itemsets; Parallel Algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527295
Filename
1527295
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