ML Using R Section 25 Eclat Association Rule learning

Section 25 Eclat Model

df <-  read.csv("G:\\RStudio\\udemy\\ml\\Machine Learning AZ\\Part 5 - Association Rule Learning\\Section 28 - Apriori\\Apriori\\Market_Basket_Optimisation.csv", header = FALSE)
head(df)

Build the sparse matrix

# install.packages("arules")
library(arules)
df <- read.transactions("G:\\RStudio\\udemy\\ml\\Machine Learning AZ\\Part 5 - Association Rule Learning\\Section 28 - Apriori\\Apriori\\Market_Basket_Optimisation.csv", sep =',', rm.duplicates = TRUE)

distribution of transactions with duplicates:
1 
5 

summary(df)

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   19   20 
1754 1358 1044  816  667  493  391  324  259  139  102   67   40   22   17    4    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

# frequency plot
itemFrequencyPlot(df, topN = 10)

Training eclat on the dataset

# set support to items that are purchased 3 times a day over the whole week 7*3/7500
# set minlen to get at least 2 items in the set.
rules <-  eclat(data = df, parameter = list(support = 0.004, minlen = 2) )

Eclat

parameter specification:
 tidLists support minlen maxlen            target   ext
    FALSE   0.004      2     10 frequent itemsets FALSE

algorithmic control:
 sparse sort verbose
      7   -2    TRUE

Absolute minimum support count: 30 

create itemset ... 
set transactions ...[119 item(s), 7501 transaction(s)] done [0.01s].
sorting and recoding items ... [114 item(s)] done [0.00s].
creating sparse bit matrix ... [114 row(s), 7501 column(s)] done [0.00s].
writing  ... [845 set(s)] done [0.04s].
Creating S4 object  ... done [0.00s].

Visualize the results

# show the top 20 rules sorted decreasing support
inspect(sort(rules, by = 'support')[1:20])

     items                             support   
[1]  {mineral water,spaghetti}         0.05972537
[2]  {chocolate,mineral water}         0.05265965
[3]  {eggs,mineral water}              0.05092654
[4]  {milk,mineral water}              0.04799360
[5]  {ground beef,mineral water}       0.04092788
[6]  {ground beef,spaghetti}           0.03919477
[7]  {chocolate,spaghetti}             0.03919477
[8]  {eggs,spaghetti}                  0.03652846
[9]  {eggs,french fries}               0.03639515
[10] {frozen vegetables,mineral water} 0.03572857
[11] {milk,spaghetti}                  0.03546194
[12] {chocolate,french fries}          0.03439541
[13] {mineral water,pancakes}          0.03372884
[14] {french fries,mineral water}      0.03372884
[15] {chocolate,eggs}                  0.03319557
[16] {chocolate,milk}                  0.03212905
[17] {green tea,mineral water}         0.03106252
[18] {eggs,milk}                       0.03079589
[19] {burgers,eggs}                    0.02879616
[20] {french fries,green tea}          0.02852953