Fast Algorithms for Mining Association Rules in Large Databases
R. Agrawal, and R. Srikant. VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases, page 487--499. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (1994)
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 thii problem
that are fundamentally different from the known algorithms.
Empirical evaluation shows 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.
%0 Conference Paper
%1 672836
%A Agrawal, Rakesh
%A Srikant, Ramakrishnan
%B VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases
%C San Francisco, CA, USA
%D 1994
%I Morgan Kaufmann Publishers Inc.
%K imported
%P 487--499
%T Fast Algorithms for Mining Association Rules in Large Databases
%X We consider the problem of discovering association rules
between items in a large database of sales transactions.
We present two new algorithms for solving thii problem
that are fundamentally different from the known algorithms.
Empirical evaluation shows 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.
%@ 1-55860-153-8
@inproceedings{672836,
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 thii problem
that are fundamentally different from the known algorithms.
Empirical evaluation shows 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.},
added-at = {2006-05-15T12:18:49.000+0200},
address = {San Francisco, CA, USA},
author = {Agrawal, Rakesh and Srikant, Ramakrishnan},
biburl = {https://www.bibsonomy.org/bibtex/2cce11d670329a38a90f625b8005dfb8d/wolfey},
booktitle = {VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases},
interhash = {960c924ccbe1ff429a30f7433ec53122},
intrahash = {cce11d670329a38a90f625b8005dfb8d},
isbn = {1-55860-153-8},
keywords = {imported},
pages = {487--499},
publisher = {Morgan Kaufmann Publishers Inc.},
timestamp = {2006-05-15T12:18:49.000+0200},
title = {Fast Algorithms for Mining Association Rules in Large Databases},
year = 1994
}