arules - Mining Association Rules and Frequent Itemsets - R package

This R package 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.

Additional packages in the arules family are: arulesViz, arulesSequences and arulesNBMiner.

Installation

• Stable CRAN version: install from within R.
• Current development version: Download package from AppVeyor or install via install_git("mhahsler/arules") (needs devtools)

Example

R> library("arules")
R> data("Adult")

## Mine association rules.
R> 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

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].

R> summary(rules)
set of 52 rules

rule length distribution (lhs + rhs):
 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       
 Min.   :0.5084   Min.   :0.9031   Min.   :0.9844  
 1st Qu.:0.5415   1st Qu.:0.9155   1st Qu.:0.9937  
 Median :0.5974   Median :0.9229   Median :0.9997  
 Mean   :0.6436   Mean   :0.9308   Mean   :1.0036  
 3rd Qu.:0.7426   3rd Qu.:0.9494   3rd Qu.:1.0057  
 Max.   :0.9533   Max.   :0.9583   Max.   :1.0586  

mining info:
  data ntransactions support confidence
 Adult         48842     0.5        0.9

R> inspect(head(sort(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

Further Information

• Development version of arules on github.
• Michael Hahsler, Bettina Grün and Kurt Hornik, arules - A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software, 14(15), 2005.
• Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977-1981, 2011.
• arules package vignette with complete examples.
• Reference manual

Maintainer: Michael Hahsler