Eclat - A strong & stylish effect

# Data Preprocessing

# install.packages('arules')

library(arules)

dataset = read.csv('Market_Basket_Optimisation.csv')

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

Output

distribution of transactions with duplicates:

1

5

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       (Other)

         1788          1348          1306          1282          1229         22405

element (itemset/transaction) length distribution:

sizes

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   18

1754 1358 1044  816  667  493  391  324  259  139  102   67   40   22   17    4    1

  19   20

   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

itemFrequencyPlot(dataset, topN = 10)

# Training Eclat on the dataset

rules = eclat(data = dataset, parameter = list(support = 0.003, minlen = 2))

Output

Eclat

parameter specification:

 tidLists support minlen maxlen            target   ext

    FALSE   0.003      2     10 frequent itemsets FALSE

algorithmic control:

 sparse sort verbose

      7   -2    TRUE

Absolute minimum support count: 22

create itemset ...

set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].

sorting and recoding items ... [115 item(s)] done [0.00s].

creating sparse bit matrix ... [115 row(s), 7501 column(s)] done [0.00s].

writing  ... [1328 set(s)] done [0.02s].

Creating S4 object  ... done [0.00s].

# Visualising the results

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

items                             support    count

[1]  {mineral water,spaghetti}         0.05972537 448 

[2]  {chocolate,mineral water}         0.05265965 395 

[3]  {eggs,mineral water}              0.05092654 382 

[4]  {milk,mineral water}              0.04799360 360 

[5]  {ground beef,mineral water}       0.04092788 307 

[6]  {ground beef,spaghetti}           0.03919477 294 

[7]  {chocolate,spaghetti}             0.03919477 294 

[8]  {eggs,spaghetti}                  0.03652846 274 

[9]  {eggs,french fries}               0.03639515 273  

[10] {frozen vegetables,mineral water} 0.03572857 268