A computational environment for mining association rules and frequent item sets

Michael Hahsler; Bettina Grün; Kurt Hornik

doi:10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822

A computational environment for mining association rules and frequent item sets

Michael Hahsler, Bettina Grün, Kurt Hornik

Statistics and Mathematics

Publikation: Working/Discussion Paper › WU Working Paper

123 Downloads (Pure)

Abstract

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)

Originalsprache	Englisch
Erscheinungsort	Vienna
Herausgeber	Institut für Statistik und Mathematik, WU Vienna University of Economics and Business
DOIs	https://doi.org/10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822
Publikationsstatus	Veröffentlicht - 2005

Publikationsreihe

Reihe	Research Report Series / Department of Statistics and Mathematics
Nummer	15

Bibliographische Notiz

Frühere Version

WU Working Paper Reihe

Research Report Series / Department of Statistics and Mathematics

Zugriff auf Dokument

10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822Lizenz: Nicht angegeben

DocumentEndgültige, publizierte Fassung, 298 KBLizenz: Nicht angegeben

Verfügbarkeit prüfen

Katalog der Universitätsbibliothek

1 Originalbeitrag in Fachzeitschrift

arules - A computational environment for mining association rules and frequent item sets
Hahsler, M., Grün, B. & Hornik, K., 2005, in: Journal of Statistical Software. 14, 15, S. 1 - 25
Publikation: Wissenschaftliche Fachzeitschrift › Originalbeitrag in Fachzeitschrift › Begutachtung

Open Access
Datei

347 Downloads (Pure)

Zitat

@techreport{384acceb9bcf4cfeb0c7f745184a0822,

title = "A computational environment for mining association rules and frequent item sets",

abstract = "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)",

author = "Michael Hahsler and Bettina Gr{\"u}n and Kurt Hornik",

note = "Earlier version",

year = "2005",

doi = "10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822",

language = "English",

series = "Research Report Series / Department of Statistics and Mathematics",

number = "15",

publisher = "Institut f{\"u}r Statistik und Mathematik, WU Vienna University of Economics and Business",

edition = "April 2005",

type = "WorkingPaper",

institution = "Institut f{\"u}r Statistik und Mathematik, WU Vienna University of Economics and Business",

}

Hahsler, M, Grün, B & Hornik, K 2005 'A computational environment for mining association rules and frequent item sets' Research Report Series / Department of Statistics and Mathematics, Nr. 15, April 2005 Aufl., Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, Vienna. https://doi.org/10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822

TY - UNPB

T1 - A computational environment for mining association rules and frequent item sets

AU - Hahsler, Michael

AU - Grün, Bettina

AU - Hornik, Kurt

N1 - Earlier version

PY - 2005

Y1 - 2005

N2 - 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)

AB - 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)

U2 - 10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822

DO - 10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822

M3 - WU Working Paper

T3 - Research Report Series / Department of Statistics and Mathematics

BT - A computational environment for mining association rules and frequent item sets

PB - Institut für Statistik und Mathematik, WU Vienna University of Economics and Business

CY - Vienna

ER -

A computational environment for mining association rules and frequent item sets

Abstract

Publikationsreihe

Bibliographische Notiz

WU Working Paper Reihe

Zugriff auf Dokument

Verfügbarkeit prüfen

Andere Versionen dieses Inhalts

arules - A computational environment for mining association rules and frequent item sets

Zitat