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arules (version 0.2-5)

Mining Association Rules and Frequent Itemsets

Description

Provides the basic infrastructure for mining and analyzing association rules and an interface to C implementations of the mining algorithms Apriori and Eclat.

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Version

Install

install.packages('arules')

Monthly Downloads

19,711

Version

0.2-5

License

GPL version 2 or later. This R package includes C code from Christian Borgelt which is distributed under the GPL version 2.1.

Maintainer

Michael Hahsler

Last Published

January 10th, 2026

Functions in arules (0.2-5)

Methods for "[": Extraction or Subsetting in Package 'arules'

Mining Associations with Apriori

Groceries Data Set

Writes transactions or associations to disk

Display Associations and Transactions in Readable Form

Class ``rules'' --- A Set of Rules

associations-class

Class ``associations'' - A Set of Associations

Income Data Set

Item Coding -- Handling Item Labels and Column IDs Conversions

read.transactions

Read Transaction Data

ASparameter-classes

Classes ``ASparameter'', ``APparameter'', ``ECparameter'' --- Specifying the `parameter' Argument of apriori() and eclat()

Sorting Associations

random.transactions

Create a Random Transaction Data Set

Class ``itemsets'' --- A Set of Itemsets

Support Counting for Itemsets

Set Operations for Associations

Find Duplicated Elements

Getting the Number of Elements

Class ``tidLists'' --- Transaction ID Lists for Items/Itemsets

Getting Frequency/Support for Single Items

Mining Associations with Eclat

AScontrol-classes

Classes ``AScontrol'', ``APcontrol'', ``ECcontrol'' --- Specifying the `control' Argument of apriori() and eclat()

APappearance-class

Class ``APappearance'' --- Specifying the `appearance' Argument of apriori()

transactions-class

Class ``transactions'' --- Binary Incidence Matrix for Transactions

Subsetting Itemsets, Rules and Transactions

itemMatrix-class

Class ``itemMatrix'' --- Sparse Binary Incidence Matrix to Represent Sets of Items

Rule Induction for a Set of Itemsets

Getting the Size of Each Element

Calculating Additional Interest Measures for Existing Associations

List Representation for Objects Based on ``itemMatrix''

Random Samples and Permutations

Remove Duplicated Elements from a Collection

Visual Inspection of Binary Incidence Matrices

Combining Objects

itemFrequencyPlot

Creating a Item Frequencies/Support Bar Plot