Extraction de règles d'association

Analyse des paniers d'achats en R

Christopher Bruffaerts

Statistician

Itemsets fréquents avec apriori

Extraire des itemsets fréquents de taille min. 2

# Extraire l'ensemble des itemsets les plus fréquents
itemsets_freq2 = 
  apriori(Groceries,
          parameter = list(supp = 0.01, 
                           minlen = 2,
                           target = 'frequent'
                         ))

Trier et inspecter les itemsets fréquents

inspect(head(sort(itemsets_freq2, by="support")))

   items                              support    count
[1] {other vegetables,whole milk}      0.07483477 736  
[2] {whole milk,rolls/buns}            0.05663447 557  
[3] {whole milk,yogurt}                0.05602440 551  
[4] {root vegetables,whole milk}       0.04890696 481  
[5] {root vegetables,other vegetables} 0.04738180 466  
[6] {other vegetables,yogurt}          0.04341637 427

Règles avec apriori

rules = apriori(Groceries, parameter = list(supp=.001,
                                            conf=.5,
                                            minlen=2,
                                            target='rules'
                 ))

inspect(head(sort(rules, by="confidence")))

   lhs                                           rhs                support     confidence lift     count
[1] {rice,sugar}                               => {whole milk}       0.001220132 1          3.913649 12   
[2] {canned fish,hygiene articles}             => {whole milk}       0.001118454 1          3.913649 11   
[3] {root vegetables,butter,rice}              => {whole milk}       0.001016777 1          3.913649 10   
[4] {root vegetables,whipped/sour cream,flour} => {whole milk}       0.001728521 1          3.913649 17   
[5] {butter,soft cheese,domestic eggs}         => {whole milk}       0.001016777 1          3.913649 10   
[6] {citrus fruit,root vegetables,soft cheese} => {other vegetables} 0.001016777 1          5.168156 10

Choisir les paramètres d'arules

Boucler sur différents seuils de confiance

# Ensemble de niveaux de confiance
confidenceLevels = seq(from=0.1, to=0.9, by =0.1)

# Créer un vecteur vide
rules_sup0005 = NULL

# Algorithme apriori avec un support de 0,5 %
for (i in 1:length(confidenceLevels)) {
  rules_sup0005[i] = 
    length(apriori(Groceries, 
                   parameter=list(supp=0.005,
                                  conf=confidenceLevels[i],
                                  target="rules")))
}

library(ggplot2)
# Nombre de règles trouvées avec un support de 0,5 %
qplot(confidenceLevels, rules_sup0005, 
      geom=c("point", "line"),xlab="Niveau de confiance",
      ylab="Nombre de règles trouvées") +
  theme_bw()

nb_rules_groceries

Créer des sous-ensembles de règles

# Sous-ensemble de règles
inspect(subset(rules, subset = 
               items %in% c("soft cheese","whole milk") &
               confidence >.95))

     lhs                                                             rhs                support     confidence lift     count
[1]  {rice,sugar}                                                 => {whole milk}       0.001220132 1          3.913649 12   
[2]  {canned fish,hygiene articles}                               => {whole milk}       0.001118454 1          3.913649 11   
[3]  {root vegetables,butter,rice}                                => {whole milk}       0.001016777 1          3.913649 10

Souplesse des sous-ensembles

inspect(subset(rules, subset=items %ain% c("soft cheese","whole milk") & confidence >.95))

inspect(subset(rules, subset=rhs %in% "whole milk" & lift >3 & confidence >0.95))

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Analyse des paniers d'achats en R