Fast algorithms for mining association rules

Fast algorithms for mining association rules (1994)

by Rakesh Agrawal , Ramakrishnan Srikant

Venue:	Proc. of 20th Intl. Conf. on VLDB
Citations:	2161 - 11 self

Datum	Value	Source
TITLE	Fast algorithms for mining association rules	INFERENCE
AUTHOR NAME	Rakesh Agrawal	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center; 650 Harry Road	SVM HeaderParse 0.2
AUTHOR ADDR	San Jose, CA 95120	SVM HeaderParse 0.2
AUTHOR NAME	Ramakrishnan Srikant	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center; 650 Harry Road	SVM HeaderParse 0.2
AUTHOR ADDR	San Jose, CA 95120	SVM HeaderParse 0.2
ABSTRACT	We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally di erent from the known algorithms. Experiments with synthetic as well as real-life data show that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database. 1	SVM HeaderParse 0.2
YEAR	1994	INFERENCE
VENUE	Proc. of 20th Intl. Conf. on VLDB	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	487--499	INFERENCE
CITATIONS	24 found	ParsCit 1.0