Apriori Algorithm in R

Apriori.R

> library(arules)
> data("Adult")
> rules <- apriori(Adult,parameter = list(supp = 0.5, conf = 0.9, target = "rules"))
> 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       
# 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

> inspect(rules) #It gives the list of all significant association rules. Some of them are shown below


#    lhs                               rhs                              support confidence      lift
# [1]  {}                             => {capital-gain=None}            0.9173867  0.9173867 1.0000000
# [2]  {}                             => {capital-loss=None}            0.9532779  0.9532779 1.0000000
# [3]  {hours-per-week=Full-time}     => {capital-gain=None}            0.5435895  0.9290688 1.0127342
# [4]  {hours-per-week=Full-time}     => {capital-loss=None}            0.5606650  0.9582531 1.0052191
# [5]  {sex=Male}                     => {capital-gain=None}            0.6050735  0.9051455 0.9866565
# [6]  {sex=Male}                     => {capital-loss=None}            0.6331027  0.9470750 0.9934931
# [7]  {workclass=Private}            => {capital-gain=None}            0.6413742  0.9239073 1.0071078
# [8]  {workclass=Private}            => {capital-loss=None}            0.6639982  0.9564974 1.0033773
# [9]  {race=White}                   => {native-country=United-States} 0.7881127  0.9217231 1.0270761
# [10] {race=White}                   => {capital-gain=None}            0.7817862  0.9143240 0.9966616