README.md

arules --- Mining Association Rules and Frequent Itemsets with R

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The arules package for R 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.

arules core packages:

Other related packages:

Additional mining algorithms

In-database analytics

Interface

Classification

Recommendation/Prediction

Installation

Stable CRAN version: install from within R with

install.packages("arules")

Current development version: Download package from AppVeyor or install from GitHub (needs devtools).

library("devtools")
install_github("mhahsler/arules")

Usage

Load package and mine some association rules.

library("arules")
data("Adult")

rules <- apriori(Adult, parameter = list(supp = 0.5, conf = 0.9, target = "rules"))
Parameter specification:
 confidence minval smax arem  aval originalSupport support minlen maxlen target   ext
        0.9    0.1    1 none FALSE            TRUE     0.5      1     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 24421 

apriori - find association rules with the apriori algorithm
version 4.21 (2004.05.09)        (c) 1996-2004   Christian Borgelt
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[115 item(s), 48842 transaction(s)] done [0.03s].
sorting and recoding items ... [9 item(s)] done [0.00s].
creating transaction tree ... done [0.03s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [52 rule(s)] done [0.00s].
creating S4 object  ... done [0.01s].

Show basic statistics.

summary(rules)
set of 52 rules

rule length distribution (lhs + rhs):sizes
 1  2  3  4 
 2 13 24 13 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   2.923   3.250   4.000 

summary of quality measures:
    support         confidence          lift            count      
 Min.   :0.5084   Min.   :0.9031   Min.   :0.9844   Min.   :24832  
 1st Qu.:0.5415   1st Qu.:0.9155   1st Qu.:0.9937   1st Qu.:26447  
 Median :0.5974   Median :0.9229   Median :0.9997   Median :29178  
 Mean   :0.6436   Mean   :0.9308   Mean   :1.0036   Mean   :31433  
 3rd Qu.:0.7426   3rd Qu.:0.9494   3rd Qu.:1.0057   3rd Qu.:36269  
 Max.   :0.9533   Max.   :0.9583   Max.   :1.0586   Max.   :46560  

mining info:
  data ntransactions support confidence
 Adult         48842     0.5        0.9

Inspect rules with the highest lift.

inspect(head(rules, by = "lift"))
    lhs                               rhs                              support confidence     lift
[1] {sex=Male,                                                                                    
     native-country=United-States} => {race=White}                   0.5415421  0.9051090 1.058554
[2] {sex=Male,                                                                                    
     capital-loss=None,                                                                           
     native-country=United-States} => {race=White}                   0.5113632  0.9032585 1.056390
[3] {race=White}                   => {native-country=United-States} 0.7881127  0.9217231 1.027076
[4] {race=White,                                                                                  
     capital-loss=None}            => {native-country=United-States} 0.7490480  0.9205626 1.025783
[5] {race=White,                                                                                  
     sex=Male}                     => {native-country=United-States} 0.5415421  0.9204803 1.025691
[6] {race=White,                                                                                  
     capital-gain=None}            => {native-country=United-States} 0.7194628  0.9202807 1.025469

Support

Please report bugs here on GitHub. Questions should be posted on stackoverflow and tagged with arules.

References



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arules documentation built on Dec. 4, 2018, 1:04 a.m.