Title :
Parallel mining of association rules with a Hopfield type neural network
Author :
Gaber, K. ; Bahi, M.J. ; El-Ghazawi, T.
Author_Institution :
LIAL, Ecole Centrale de Lille, France
Abstract :
Association rule mining (ARM) is one of the data mining problems receiving a great deal of attention in the database community. The main computation step in an ARM algorithm is frequent itemset discovery. In this paper, a frequent itemset discovery algorithm based on the Hopfield model is presented
Keywords :
Hopfield neural nets; data mining; very large databases; ARM algorithm; Hopfield neural network; data mining; frequent itemset discovery algorithm; large database; parallel association rule mining; Association rules; Data mining; Databases; Economic forecasting; Electronic mail; Hopfield neural networks; Itemsets; Iterative algorithms; Neural networks; Partitioning algorithms;
Conference_Titel :
Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-0909-6
DOI :
10.1109/TAI.2000.889851
