A computational environment for mining association rules and frequent item sets

Mining frequent itemsets and association rules is a popular and well researched approach to discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for a...

Full description

Bibliographic Details
Main Authors:	Hahsler, Michael, Grün, Bettina, Hornik, Kurt
Format:	Others
Language:	en
Published:	Institut für Statistik und Mathematik, WU Vienna University of Economics and Business 2005
Subjects:	Data Mining / Association Rules / Frequent Itemsets / Implementation
Online Access:	http://epub.wu.ac.at/132/1/document.pdf

id	ndltd-VIENNA-oai-epub.wu-wien.ac.at-epub-wu-01_821
record_format	oai_dc
spelling	ndltd-VIENNA-oai-epub.wu-wien.ac.at-epub-wu-01_8212013-09-28T04:37:34Z A computational environment for mining association rules and frequent item sets Hahsler, Michael Grün, Bettina Hornik, Kurt Data Mining / Association Rules / Frequent Itemsets / Implementation Mining frequent itemsets and association rules is a popular and well researched approach to discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules. (author's abstract) Institut für Statistik und Mathematik, WU Vienna University of Economics and Business 2005 Paper NonPeerReviewed en application/pdf http://epub.wu.ac.at/132/1/document.pdf Series: Research Report Series / Department of Statistics and Mathematics http://epub.wu.ac.at/132/
collection	NDLTD
language	en
format	Others
sources	NDLTD
topic	Data Mining / Association Rules / Frequent Itemsets / Implementation
spellingShingle	Data Mining / Association Rules / Frequent Itemsets / Implementation Hahsler, Michael Grün, Bettina Hornik, Kurt A computational environment for mining association rules and frequent item sets
description	Mining frequent itemsets and association rules is a popular and well researched approach to discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules. (author's abstract) === Series: Research Report Series / Department of Statistics and Mathematics
author	Hahsler, Michael Grün, Bettina Hornik, Kurt
author_facet	Hahsler, Michael Grün, Bettina Hornik, Kurt
author_sort	Hahsler, Michael
title	A computational environment for mining association rules and frequent item sets
title_short	A computational environment for mining association rules and frequent item sets
title_full	A computational environment for mining association rules and frequent item sets
title_fullStr	A computational environment for mining association rules and frequent item sets
title_full_unstemmed	A computational environment for mining association rules and frequent item sets
title_sort	computational environment for mining association rules and frequent item sets
publisher	Institut für Statistik und Mathematik, WU Vienna University of Economics and Business
publishDate	2005
url	http://epub.wu.ac.at/132/1/document.pdf
work_keys_str_mv	AT hahslermichael acomputationalenvironmentforminingassociationrulesandfrequentitemsets AT grunbettina acomputationalenvironmentforminingassociationrulesandfrequentitemsets AT hornikkurt acomputationalenvironmentforminingassociationrulesandfrequentitemsets AT hahslermichael computationalenvironmentforminingassociationrulesandfrequentitemsets AT grunbettina computationalenvironmentforminingassociationrulesandfrequentitemsets AT hornikkurt computationalenvironmentforminingassociationrulesandfrequentitemsets
_version_	1716598150705184768

A computational environment for mining association rules and frequent item sets

Similar Items