Store are interested in determining the associations between items purchased from its Departments. The store chose to conduct a market basket analysis of specific items purchased to analyze customer’s buying behavior. dataset provided is ‘transactions.csv’ containing information for transactions made over the past 3 months

# read the transactional data
transactions <- read.transactions("transactions.csv", format = 'basket', sep = ',')
inspect(head(transactions))

summary(transactions)

arules::itemFrequencyPlot(transactions, topN = 10,
                          col = brewer.pal(8, 'Pastel2'),
                          main = 'absolute Item Frequency Plot',
                          type = "absolute",
                          ylab = "Item Frequency (absolute)")

• Generate the association rules using:
  • Minimum support of 0.002
  • Minimum confidence of 0.20
  • Maximum length of 3

association_rule_len3 <- apriori(transactions,
                 parameter = list(supp = 0.002, conf = 0.2,maxlen=3));

Sort the rules descendingly by the lift value

association_rule_len3_sorted <- sort(association_rule_len3, decreasing = TRUE, by = "lift")
inspect(association_rule_len3_sorted[1:10])

• Generate the association rules using:
  • Minimum support of 0.002
  • Minimum confidence of 0.20
  • Maximum length of 2

association_rule_len2 <- apriori(transactions,
                 parameter = list(supp = 0.002, conf = 0.2,maxlen=2))

# Sort the rules descendingly by the lift value
association_rule_len2_sorted <- sort(association_rule_len2, decreasing = TRUE, by = "lift")
inspect(association_rule_len2_sorted[1:10])

The highest lift rules of maxlen =3 and maxlen=2

# Maxlen=3
inspect(association_rule_len3_sorted[1])

# Maxlen = 2
inspect(association_rule_len2_sorted[1])

Important Note: As known the support and confidence are insufficient at filtering out uninteresting rules. so, the measure of the goodness of an association rule is the lift value. The higher lift the better rule.