R Notebook

##### Chapter 8: Association Rules -------------------
## Example: Identifying Frequently-Purchased Groceries ----
## Step 2: Exploring and preparing the data ----
# load the grocery data into a sparse matrix
library(arules)
groceries <- read.transactions("http://www.sci.csueastbay.edu/~esuess/classes/Statistics_6620/Presentations/ml13/groceries.csv", sep = ",")
summary(groceries)

transactions as itemMatrix in sparse format with
 9835 rows (elements/itemsets/transactions) and
 169 columns (items) and a density of 0.02609145577 

most frequent items:
      whole milk other vegetables       rolls/buns             soda           yogurt 
            2513             1903             1809             1715             1372 
         (Other) 
           34055 

element (itemset/transaction) length distribution:
sizes
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
2159 1643 1299 1005  855  645  545  438  350  246  182  117   78   77   55   46   29   14 
  19   20   21   22   23   24   26   27   28   29   32 
  14    9   11    4    6    1    1    1    1    3    1 

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
 1.000000  2.000000  3.000000  4.409456  6.000000 32.000000 

includes extended item information - examples:
            labels
1 abrasive cleaner
2 artif. sweetener
3   baby cosmetics

# look at the first five transactions
inspect(groceries[1:5])

    items                     
[1] {citrus fruit,            
     margarine,               
     ready soups,             
     semi-finished bread}     
[2] {coffee,                  
     tropical fruit,          
     yogurt}                  
[3] {whole milk}              
[4] {cream cheese,            
     meat spreads,            
     pip fruit,               
     yogurt}                  
[5] {condensed milk,          
     long life bakery product,
     other vegetables,        
     whole milk}              

# examine the frequency of items
itemFrequency(groceries[, 1:3])

abrasive cleaner artif. sweetener   baby cosmetics 
 0.0035587188612  0.0032536858160  0.0006100660905 

# plot the frequency of items
itemFrequencyPlot(groceries, support = 0.1)

itemFrequencyPlot(groceries, topN = 20)

# a visualization of the sparse matrix for the first five transactions
image(groceries[1:5])

# visualization of a random sample of 100 transactions
image(sample(groceries, 100))

## Step 3: Training a model on the data ----
library(arules)
# default settings result in zero rules learned
apriori(groceries)

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen maxlen target   ext
        0.8    0.1    1 none FALSE            TRUE       5     0.1      1     10  rules FALSE

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

Absolute minimum support count: 983 

set item appearances ...[0 item(s)] done [0.01s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.02s].
sorting and recoding items ... [8 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.01s].
writing ... [0 rule(s)] done [0.01s].
creating S4 object  ... done [0.01s].
set of 0 rules 

# set better support and confidence levels to learn more rules
groceryrules <- apriori(groceries, parameter = list(support =
                          0.006, confidence = 0.25, minlen = 2))

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen maxlen target   ext
       0.25    0.1    1 none FALSE            TRUE       5   0.006      2     10  rules FALSE

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

Absolute minimum support count: 59 

set item appearances ...[0 item(s)] done [0.01s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.02s].
sorting and recoding items ... [109 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.02s].
writing ... [463 rule(s)] done [0.01s].
creating S4 object  ... done [0.01s].

groceryrules

set of 463 rules 

## Step 4: Evaluating model performance ----
# summary of grocery association rules
summary(groceryrules)

set of 463 rules

rule length distribution (lhs + rhs):sizes
  2   3   4 
150 297  16 

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
2.000000 2.000000 3.000000 2.710583 3.000000 4.000000 

summary of quality measures:
    support              confidence             lift          
 Min.   :0.006100661   Min.   :0.2500000   Min.   :0.9932367  
 1st Qu.:0.007117438   1st Qu.:0.2970711   1st Qu.:1.6229230  
 Median :0.008744281   Median :0.3553719   Median :1.9332351  
 Mean   :0.011539429   Mean   :0.3785573   Mean   :2.0350922  
 3rd Qu.:0.012302999   3rd Qu.:0.4494849   3rd Qu.:2.3564791  
 Max.   :0.074834774   Max.   :0.6600000   Max.   :3.9564774  

mining info:
      data ntransactions support confidence
 groceries          9835   0.006       0.25

# look at the first three rules
inspect(groceryrules[1:3])

    lhs                rhs               support        confidence   lift       
[1] {potted plants} => {whole milk}      0.006914082359 0.4000000000 1.565459610
[2] {pasta}         => {whole milk}      0.006100660905 0.4054054054 1.586614470
[3] {herbs}         => {root vegetables} 0.007015760041 0.4312500000 3.956477379

## Step 5: Improving model performance ----
# sorting grocery rules by lift
inspect(sort(groceryrules, by = "lift")[1:5])

    lhs                   rhs                         support   confidence        lift
[1] {herbs}            => {root vegetables}    0.007015760041 0.4312500000 3.956477379
[2] {berries}          => {whipped/sour cream} 0.009049313676 0.2721712538 3.796885505
[3] {other vegetables,                                                                
     tropical fruit,                                                                  
     whole milk}       => {root vegetables}    0.007015760041 0.4107142857 3.768073694
[4] {beef,                                                                            
     other vegetables} => {root vegetables}    0.007930859176 0.4020618557 3.688692491
[5] {other vegetables,                                                                
     tropical fruit}   => {pip fruit}          0.009456024403 0.2634560907 3.482648725

# finding subsets of rules containing any berry items
berryrules <- subset(groceryrules, items %in% "berries")
inspect(berryrules)

    lhs          rhs                  support        confidence   lift       
[1] {berries} => {whipped/sour cream} 0.009049313676 0.2721712538 3.796885505
[2] {berries} => {yogurt}             0.010574478902 0.3180428135 2.279847719
[3] {berries} => {other vegetables}   0.010269445857 0.3088685015 1.596280459
[4] {berries} => {whole milk}         0.011794611083 0.3547400612 1.388328095

# writing the rules to a CSV file
write(groceryrules, file = "groceryrules.csv",
      sep = ",", quote = TRUE, row.names = FALSE)
# converting the rule set to a data frame
groceryrules_df <- as(groceryrules, "data.frame")
str(groceryrules_df)

'data.frame':   463 obs. of  4 variables:
 $ rules     : Factor w/ 463 levels "{baking powder} => {other vegetables}",..: 340 302 207 206 208 341 402 21 139 140 ...
 $ support   : num  0.00691 0.0061 0.00702 0.00773 0.00773 ...
 $ confidence: num  0.4 0.405 0.431 0.475 0.475 ...
 $ lift      : num  1.57 1.59 3.96 2.45 1.86 ...