Package index • arules

Package overview

Infrastructure for representing, manipulating, and analyzing transaction data, frequent itemsets, and association rules.

arules arules-package: Infrastructure for representing transaction data, mining frequent itemsets and association rules, and evaluating the resulting patterns. The package includes efficient C implementations of the Apriori and Eclat algorithms.

Transaction data and item matrices

Represent, inspect, visualize, and manipulate sparse binary incidence matrices, transactions, and transaction ID lists.

transactions() summary(<transactions>) toLongFormat(<transactions>) items(<transactions>) transactionInfo() `transactionInfo<-`() dimnames(<transactions>) `dimnames<-`(<transactions>,<list>): Class transactions — Binary Incidence Matrix for Transactions
abbreviate(): Abbreviate item labels in transactions, itemMatrix and associations
c(<itemMatrix>) c(<transactions>) c(<tidLists>) c(<rules>) c(<itemsets>): Combining Association and Transaction Objects
crossTable(): Cross-tabulate joint occurrences across pairs of items
duplicated(): Find Duplicated Elements
`[`(<itemMatrix>,<ANY>,<ANY>,<ANY>) `[`(<transactions>,<ANY>,<ANY>,<ANY>) `[`(<tidLists>,<ANY>,<ANY>,<ANY>) `[`(<rules>,<ANY>,<ANY>,<ANY>) `[`(<itemsets>,<ANY>,<ANY>,<ANY>): Methods for "[": Extraction or Subsetting arules Objects
addAggregate() filterAggregate() aggregate(): Support for Item Hierarchies
image(<itemMatrix>) image(<transactions>) image(<tidLists>): Visual Inspection of Binary Incidence Matrices
inspect(): Display Associations and Transactions in Readable Form
is.superset() is.subset(): Find Super and Subsets
itemFrequency(): Getting Frequency/Support for Single Items
itemFrequencyPlot(): Creating a Item Frequencies/Support Bar Plot
summary(<itemMatrix>) dim(<itemMatrix>) nitems() length(<itemMatrix>) toLongFormat() labels(<itemMatrix>) itemLabels() `itemLabels<-`() itemInfo() `itemInfo<-`() itemsetInfo() `itemsetInfo<-`() dimnames(<itemMatrix>) `dimnames<-`(<itemMatrix>,<list>): Class itemMatrix — Sparse Binary Incidence Matrix to Represent Sets of Items
itemUnion() itemSetdiff() itemIntersect(): Itemwise Set Operations
match() `%in%`(<itemMatrix>,<itemMatrix>) `%in%`(<itemMatrix>,<character>) `%in%`(<associations>,<associations>) `%pin%`(<itemMatrix>,<character>) `%ain%`(<itemMatrix>,<character>) `%oin%`(<itemMatrix>,<character>): Value Matching
merge(): Adding Items to Data
random.transactions() random.patterns(): Simulate a Random Transactions
sample(<itemMatrix>) sample(<associations>): Random Samples and Permutations
union(<itemMatrix>) union(<associations>) intersect(<itemMatrix>) intersect(<associations>) setequal(<itemMatrix>) setequal(<associations>) setdiff(<itemMatrix>) setdiff(<associations>) is.element(<itemMatrix>) is.element(<associations>): Set Operations
size(): Number of Items in Sets
supportingTransactions(): Supporting Transactions
tidLists() summary(<tidLists>) dim(<tidLists>) dimnames(<tidLists>) `dimnames<-`(<tidLists>,<list>) length(<tidLists>) t(<tidLists>) transactionInfo(<tidLists>) `transactionInfo<-`(<tidLists>) itemInfo(<tidLists>) `itemInfo<-`(<tidLists>) itemLabels(<tidLists>) labels(<tidLists>): Class tidLists — Transaction ID Lists for Items/Itemsets
unique(): Remove Duplicated Elements from a Collection
decode() encode() recode() compatible(): Item Coding — Conversion between Item Labels and Column IDs
addComplement(): Add Complement-items to Transactions
subset(): Subsetting Itemsets, Rules and Transactions

Data preprocessing

Prepare data for mining by discretizing variables, managing item coding and hierarchies, adding items, and sampling data.

discretize() discretizeDF(): Convert a Continuous Variable into a Categorical Variable
addAggregate() filterAggregate() aggregate(): Support for Item Hierarchies
decode() encode() recode() compatible(): Item Coding — Conversion between Item Labels and Column IDs
merge(): Adding Items to Data
sample(<itemMatrix>) sample(<associations>): Random Samples and Permutations

Association mining

Mine frequent itemsets and association rules, configure the mining algorithms, and induce rules from itemsets.

apriori(): Mining Associations with the Apriori Algorithm
eclat(): Mining Associations with Eclat
weclat(): Mining Associations from Weighted Transaction Data with Eclat (WARM)
fim4r(): Interface to Mining Algorithms from fim4r
ruleInduction(): Association Rule Induction from Itemsets
APappearance-class APappearance coercion-APappearance coerce,NULL,APappearance-method coerce,list,APappearance-method: Class APappearance — Specifying the appearance Argument of Apriori to Implement Rule Templates
coerce(<NULL>,<APcontrol>): Classes AScontrol, APcontrol, ECcontrol — Specifying the control Argument of Apriori and Eclat
ASparameter-classes parameter initialize,ASparameter-method show,ASparameter-method ASparameter-class ASparameter APparameter-class APparameter initialize,APparameter-method ECparameter-class ECparameter initialize,ECparameter-method coercion coerce,NULL,APparameter-method coerce,list,APparameter-method coerce,NULL,ECparameter-method coerce,list,ECparameter-method: Classes ASparameter, APparameter, ECparameter — Specifying the parameter Argument of APRIORI and ECLAT

Weighted association mining

Compute transaction weights and mine frequent itemsets from weighted transaction data.

SunBai sunbai: The SunBai Weighted Transactions Data Set
hits(): Computing Transaction Weights With HITS
weclat(): Mining Associations from Weighted Transaction Data with Eclat (WARM)

Associations and set operations

Represent and manipulate sets of itemsets and association rules.

inspect(): Display Associations and Transactions in Readable Form
abbreviate(): Abbreviate item labels in transactions, itemMatrix and associations
quality(<associations>) `quality<-`(<associations>) info(<associations>) `info<-`(<associations>) head(<associations>) tail(<associations>) items(<associations>) length(<associations>) labels(<associations>) plot(<associations>) plot(<itemMatrix>): Class associations — A Set of Associations
c(<itemMatrix>) c(<transactions>) c(<tidLists>) c(<rules>) c(<itemsets>): Combining Association and Transaction Objects
duplicated(): Find Duplicated Elements
`[`(<itemMatrix>,<ANY>,<ANY>,<ANY>) `[`(<transactions>,<ANY>,<ANY>,<ANY>) `[`(<tidLists>,<ANY>,<ANY>,<ANY>) `[`(<rules>,<ANY>,<ANY>,<ANY>) `[`(<itemsets>,<ANY>,<ANY>,<ANY>): Methods for "[": Extraction or Subsetting arules Objects
is.closed(): Find Closed Itemsets
is.generator(): Find Generator Itemsets
is.maximal(): Find Maximal Itemsets
is.redundant(): Find Redundant Rules
is.significant(): Find Significant Rules
is.superset() is.subset(): Find Super and Subsets
itemsets() summary(<itemsets>) length(<itemsets>) nitems(<itemsets>) labels(<itemsets>) itemLabels(<itemsets>) `itemLabels<-`(<itemsets>) itemInfo(<itemsets>) items(<itemsets>) `items<-`(<itemsets>) tidLists(<itemsets>): Class itemsets — A Set of Itemsets
match() `%in%`(<itemMatrix>,<itemMatrix>) `%in%`(<itemMatrix>,<character>) `%in%`(<associations>,<associations>) `%pin%`(<itemMatrix>,<character>) `%ain%`(<itemMatrix>,<character>) `%oin%`(<itemMatrix>,<character>): Value Matching
rules() summary(<rules>) length(<rules>) nitems(<rules>) labels(<rules>) itemLabels(<rules>) `itemLabels<-`(<rules>) itemInfo(<rules>) lhs() `lhs<-`() rhs() `rhs<-`() items(<rules>) generatingItemsets(): Class rules — A Set of Rules
sample(<itemMatrix>) sample(<associations>): Random Samples and Permutations
union(<itemMatrix>) union(<associations>) intersect(<itemMatrix>) intersect(<associations>) setequal(<itemMatrix>) setequal(<associations>) setdiff(<itemMatrix>) setdiff(<associations>) is.element(<itemMatrix>) is.element(<associations>): Set Operations
size(): Number of Items in Sets
sort(<associations>): Sort Associations
unique(): Remove Duplicated Elements from a Collection

Interest measures

Calculate support, coverage, confidence intervals, and other interest measures for itemsets and association rules.

confint(<rules>): Confidence Intervals for Interest Measures for Association Rules
coverage(): Calculate coverage for rules
interestMeasure(): Calculate Additional Interest Measures
is.redundant(): Find Redundant Rules
is.significant(): Find Significant Rules
support(): Support Counting for Itemsets

Postprocessing

Induce and prune rules and identify closed, generator, maximal, redundant, significant, and related itemsets or rules.

interestMeasure(): Calculate Additional Interest Measures
is.closed(): Find Closed Itemsets
is.generator(): Find Generator Itemsets
is.maximal(): Find Maximal Itemsets
is.redundant(): Find Redundant Rules
is.significant(): Find Significant Rules
is.superset() is.subset(): Find Super and Subsets
ruleInduction(): Association Rule Induction from Itemsets

Proximities and prediction

Compute and represent affinities, similarities, and dissimilarities and use them for nearest-neighbor prediction.

affinity(): Computing Affinity Between Items
dissimilarity(): Dissimilarity Matrix Computation for Associations and Transactions
predict(): Model Predictions
proximity-classes ar_similarity-class ar_cross_dissimilarity-class: Classes dist, ar_cross_dissimilarity and ar_similarity — Proximity Matrices

Import and export

Read, write, and convert transactions and associations using data frames, lists, text formats, and PMML.

DATAFRAME(): Data.frame Representation for arules Objects
LIST(): List Representation for Objects Based on Class itemMatrix
write.PMML() read.PMML(): Read and Write PMML
read.transactions(): Read Transaction Data
write(): Write Transactions or Associations to a File

Data sets

Example transaction and survey data sets for association mining and preprocessing.

Adult adult AdultUCI: Adult Data Set
Epub: The Epub Transactions Data Set
Groceries groceries: The Groceries Transactions Data Set
Income income IncomeESL: The Income Data Set
Mushroom mushroom: The Mushroom Data Set as Transactions
SunBai sunbai: The SunBai Weighted Transactions Data Set