[R] Eclat.ipynb

Kernel: R

Eclat

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)

Loading required package: Matrix

Attaching package: ‘arules’

The following objects are masked from ‘package:base’:

    abbreviate, write

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 Eclat on the dataset

min_support = 4*7/7500
min_support

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

Eclat

parameter specification:
 tidLists     support minlen maxlen            target   ext
    FALSE 0.003733333      2     10 frequent itemsets FALSE

algorithmic control:
 sparse sort verbose
      7   -2    TRUE

Absolute minimum support count: 28 

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  ... [939 set(s)] done [0.02s].
Creating S4 object  ... done [0.00s].

Visualization of the result

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