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arules (version 1.3-1)

Mining Association Rules and Frequent Itemsets

Description

Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides interfaces to C implementations of the association mining algorithms Apriori and Eclat by C. Borgelt.

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Version

Install

install.packages('arules')

Monthly Downloads

11,895

Version

1.3-1

License

GPL-3

Maintainer

Michael Hahsler

Last Published

December 14th, 2015

Functions in arules (1.3-1)

Income

Income Data Set
affinity

Computing Affinity Between Items
[-methods

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

Subsetting Itemsets, Rules and Transactions
merge

Merging (adding) items
Adult

Adult Data Set
sample

Random Samples and Permutations
Groceries

Groceries Data Set
ruleInduction

Rule Induction for a Set of Itemsets
is.significant

Find Significant Rules
tidLists-class

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

Computing Transaction Weights With HITS
weclat

Mining Associations from Weighted Transaction Data with Eclat
LIST

List Representation for Objects Based on ``itemMatrix''
ASparameter-classes

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

Visual Inspection of Binary Incidence Matrices
support

Support Counting for Itemsets
abbreviate

Abbreviate function for item labels in transactions, itemMatrix and associations
unique

Remove Duplicated Elements from a Collection
is.superset

Find Super and Subsets
combine

Combining Objects
read.transactions

Read Transaction Data
hierarchy

Support for Item Hierarchies
Epub

Epub Data Set
coverage

Calculate coverage for rules
transactions-class

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

Add Complement-items to Transactions
AScontrol-classes

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

Dissimilarity Computation
crossTable

Cross-tabulate joint occurrences across pairs of items
duplicated

Find Duplicated Elements
sort

Sorting Associations
proximity-classes

Classes ``dist'', ``ar_cross_dissimilarity'' and ``ar_similarity'' --- Proximity Matrices
apriori

Mining Associations with Apriori
itemFrequency

Getting Frequency/Support for Single Items
setOperations

Set Operations
match

Value Matching
size

Getting the Size of Each Element
inspect

Display Associations and Transactions in Readable Form
read.PMML

Read and Write PMML
length

Getting the Number of Elements
discretize

Convert a Continuous Variable into a Categorical Variable
predict

Model Predictions
is.closed

Find Closed Itemsets
supportingTransactions

Supporting Transactions
is.maximal

Find Maximal Itemsets
random.transactions

Simulate a Random Transaction Data Set
itemSetOperations

Itemwise Set Operations
APappearance-class

Class ``APappearance'' --- Specifying the `appearance' Argument of apriori() to Implement Rule Templates
SunBai

The SunBai Data Set
rules-class

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

Calculating various additional interest measures
eclat

Mining Associations with Eclat
is.redundant

Find Redundant Rules
itemFrequencyPlot

Creating a Item Frequencies/Support Bar Plot
associations-class

Class ``associations'' - A Set of Associations
write

Writes transactions or associations to disk
Mushroom

Mushroom Data Set
itemsets-class

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

Item Coding -- Handling Item Labels and Column IDs Conversions
itemMatrix-class

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