[R] Apriori.ipynb

Kernel: R

Apriori

Data Preprocessing

dataset = read.csv('Market_Basket_Optimisation.csv', header = FALSE)
# There is no header in dataset

head(dataset, 10)

dim(dataset)

---

Data contain 120 products.

---

# install.packages('arules')
library(arules)

dataset = read.transactions('Market_Basket_Optimisation.csv', 
                            sep = ',', 
                            rm.duplicates =  TRUE)

distribution of transactions with duplicates:
1 
5 

---

There are 5 transactions with 1 duplicates each.

---

summary(dataset)

transactions as itemMatrix in sparse format with
 7501 rows (elements/itemsets/transactions) and
 119 columns (items) and a density of 0.03288973 

most frequent items:
mineral water          eggs     spaghetti  french fries     chocolate 
         1788          1348          1306          1282          1229 
      (Other) 
        22405 

element (itemset/transaction) length distribution:
sizes
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1754 1358 1044  816  667  493  391  324  259  139  102   67   40   22   17    4 
  18   19   20 
   1    2    1 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   3.914   5.000  20.000 

includes extended item information - examples:
             labels
1           almonds
2 antioxydant juice
3         asparagus

# Top 10 sale items
itemFrequencyPlot(dataset, topN = 10)

Training Apriori on the dataset

min_support = 4*7/7500
min_support

rules = apriori(data = dataset, 
                parameter = list(support = min_support, confidence = 0.2))

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime     support minlen
        0.2    0.1    1 none FALSE            TRUE       5 0.003733333      1
 maxlen target   ext
     10  rules FALSE

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

Absolute minimum support count: 28 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
sorting and recoding items ... [115 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [916 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

Visualization of the result

inspect(sort(rules, by = 'lift')[1:10])

     lhs                                  rhs             support    
[1]  {mineral water,whole wheat pasta} => {olive oil}     0.003866151
[2]  {light cream}                     => {chicken}       0.004532729
[3]  {pasta}                           => {escalope}      0.005865885
[4]  {pasta}                           => {shrimp}        0.005065991
[5]  {chocolate,herb & pepper}         => {ground beef}   0.003999467
[6]  {eggs,ground beef}                => {herb & pepper} 0.004132782
[7]  {whole wheat pasta}               => {olive oil}     0.007998933
[8]  {herb & pepper,spaghetti}         => {ground beef}   0.006399147
[9]  {herb & pepper,mineral water}     => {ground beef}   0.006665778
[10] {frozen vegetables,soup}          => {milk}          0.003999467
     confidence lift     count
[1]  0.4027778  6.115863 29   
[2]  0.2905983  4.843951 34   
[3]  0.3728814  4.700812 44   
[4]  0.3220339  4.506672 38   
[5]  0.4411765  4.490183 30   
[6]  0.2066667  4.178455 31   
[7]  0.2714932  4.122410 60   
[8]  0.3934426  4.004360 48   
[9]  0.3906250  3.975683 50   
[10] 0.5000000  3.858539 30