DocumentCode
1279198
Title
Parallel mining of association rules
Author
Agrawal, Rakesh ; Shafer, John C.
Author_Institution
IBM Almaden Res. Center, San Jose, CA, USA
Volume
8
Issue
6
fYear
1996
fDate
12/1/1996 12:00:00 AM
Firstpage
962
Lastpage
969
Abstract
We consider the problem of mining association rules on a shared nothing multiprocessor. We present three algorithms that explore a spectrum of trade-offs between computation, communication, memory usage, synchronization, and the use of problem specific information. The best algorithm exhibits near perfect scaleup behavior, yet requires only minimal overhead compared to the current best serial algorithm
Keywords
deductive databases; knowledge acquisition; knowledge based systems; multiprocessing systems; parallel algorithms; very large databases; association rules; best serial algorithm; data mining; knowledge acquisition; minimal overhead; parallel algorithms; parallel mining; problem specific information; scaleup behavior; shared nothing multiprocessor; Association rules; Data mining; Data structures; Itemsets; Transaction databases;
fLanguage
English
Journal_Title
Knowledge and Data Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1041-4347
Type
jour
DOI
10.1109/69.553164
Filename
553164
Link To Document