Title :
Parallel mining of association rules
Author :
Agrawal, Rakesh ; Shafer, John C.
Author_Institution :
IBM Almaden Res. Center, San Jose, CA, USA
fDate :
12/1/1996 12:00:00 AM
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;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
