Step 1: Importing the Data
groceries<-read.csv("https://raw.githubusercontent.com/raoy/data/master/groceries.csv", header=F, sep=",")head(groceries)## V1 V2 V3 V4
## 1 citrus fruit semi-finished bread margarine ready soups
## 2 tropical fruit yogurt coffee
## 3 whole milk
## 4 pip fruit yogurt cream cheese meat spreads
## 5 other vegetables whole milk condensed milk long life bakery product
## 6 whole milk butter yogurt riceStep 2: Exploring & Preparing the Data
#install.packages("arules")
library(arules)
groceries<-read.transactions("https://raw.githubusercontent.com/raoy/data/master/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 0.02609146
##
## most frequent items:
## whole milk other vegetables rolls/buns soda
## 2513 1903 1809 1715
## yogurt (Other)
## 1372 34055
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55 46
## 17 18 19 20 21 22 23 24 26 27 28 29 32
## 29 14 14 9 11 4 6 1 1 1 1 3 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 4.409 6.000 32.000
##
## includes extended item information - examples:
## labels
## 1 abrasive cleaner
## 2 artif. sweetener
## 3 baby cosmeticsinspect(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}itemFrequency(groceries[,1:3])## abrasive cleaner artif. sweetener baby cosmetics
## 0.0035587189 0.0032536858 0.0006100661Visualizing item support
itemFrequencyPlot(groceries, support=0.1)
itemFrequencyPlot(groceries,topN=20)
Visualizing transaction data - plotting the sparse matrix
image(groceries[1:5])
image(sample(groceries,100))
Step 3: Training a model on data
#apriori(groceries)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
## 0.25 0.1 1 none FALSE TRUE 5 0.006 2
## maxlen target ext
## 10 rules TRUE
##
## 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 [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## 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 [0.00s].
## writing ... [463 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].groceryrules## set of 463 rulesStep 4: Evaluating model performance
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.
## 2.000 2.000 3.000 2.711 3.000 4.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.006101 Min. :0.2500 Min. :0.009964 Min. :0.9932
## 1st Qu.:0.007117 1st Qu.:0.2971 1st Qu.:0.018709 1st Qu.:1.6229
## Median :0.008744 Median :0.3554 Median :0.024809 Median :1.9332
## Mean :0.011539 Mean :0.3786 Mean :0.032608 Mean :2.0351
## 3rd Qu.:0.012303 3rd Qu.:0.4495 3rd Qu.:0.035892 3rd Qu.:2.3565
## Max. :0.074835 Max. :0.6600 Max. :0.255516 Max. :3.9565
## count
## Min. : 60.0
## 1st Qu.: 70.0
## Median : 86.0
## Mean :113.5
## 3rd Qu.:121.0
## Max. :736.0
##
## mining info:
## data ntransactions support confidence
## groceries 9835 0.006 0.25
## call
## apriori(data = groceries, parameter = list(support = 0.006, confidence = 0.25, minlen = 2))Looking at a specific rule
inspect(groceryrules[1:3])## lhs rhs support confidence coverage
## [1] {pot plants} => {whole milk} 0.006914082 0.4000000 0.01728521
## [2] {pasta} => {whole milk} 0.006100661 0.4054054 0.01504830
## [3] {herbs} => {root vegetables} 0.007015760 0.4312500 0.01626843
## lift count
## [1] 1.565460 68
## [2] 1.586614 60
## [3] 3.956477 69Step 5: Improving model performance
Sorting the set of association rules
inspect(sort(groceryrules, by="lift")[1:5])## lhs rhs support confidence coverage lift count
## [1] {herbs} => {root vegetables} 0.007015760 0.4312500 0.01626843 3.956477 69
## [2] {berries} => {whipped/sour cream} 0.009049314 0.2721713 0.03324860 3.796886 89
## [3] {other vegetables,
## tropical fruit,
## whole milk} => {root vegetables} 0.007015760 0.4107143 0.01708185 3.768074 69
## [4] {beef,
## other vegetables} => {root vegetables} 0.007930859 0.4020619 0.01972547 3.688692 78
## [5] {other vegetables,
## tropical fruit} => {pip fruit} 0.009456024 0.2634561 0.03589222 3.482649 93Taking subsets of association rules
berryrules<-subset(groceryrules, items %in% "berries")
inspect(berryrules)## lhs rhs support confidence coverage lift
## [1] {berries} => {whipped/sour cream} 0.009049314 0.2721713 0.0332486 3.796886
## [2] {berries} => {yogurt} 0.010574479 0.3180428 0.0332486 2.279848
## [3] {berries} => {other vegetables} 0.010269446 0.3088685 0.0332486 1.596280
## [4] {berries} => {whole milk} 0.011794611 0.3547401 0.0332486 1.388328
## count
## [1] 89
## [2] 104
## [3] 101
## [4] 116We can add other measure to limit the subsets,such as confidence>0.50. Also, we can export the rules into csv files for further investigation.
Reference
Lantz, B. (2013). Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Packt Publishing.