install.packages("tidyverse")
library(tidyverse)
# install.packages("arules")
library(arules)
# install.packages("arulesViz")
library(arulesViz)
# prepare for transaction data
my_basket1 <- read.transactions("GroceryStore_Basket.csv", format="basket", sep=",")
my_sentiment <-
my_basket1
inspect(my_basket1)
my_basket2 <- read.transactions("GroceryStore_Single.csv", format="single", sep=",", cols = c("TransactionID","Item"), header= TRUE)
inspect(my_basket2)
## (1) Import "Online Retail.csv" as a transaction data
summary(my_basket2)
itemFrequencyPlot(my_basket2)
rules <- apriori(my_basket2, parameter = list(supp=0.01, conf=0.8, maxlen =4))
summary(rules)
inspect(rules)
rules <- sort(rules, by = 'confidence', decreasing = TRUE)
inspect(rules[1:10])
itemFrequencyPlot(my_basket2)
## (2) Summarize and visualize transaction data
rules <- apriori(my_basket2, parameter=list(supp=0.01, conf=0.8, maxlen=4, minlen=2))
summary(rules)
inspect(rules)
rules <- sort(rules, by = "confidence")
## (3) Apply the Apriori algorithm
## Remove redundant rules
is.redundant(rules)
inspect(rules[is.redundant(rules)])
rule2 <- rules[!is.redundant(rules)]
inspect(rules2)
plot(rules2)
plot(rules2, method = "graph")
plot(rules[1:10], method = "graph")
bread_rules <- apriori(my_basket2, parameter = list(supp=0.01, conf=0.8, maxlen=4), appearance=list(default="lhs", rhs = "BREAD"))
bread_rules <- sort(bread_rules, by = "confidence", decreasing = TRUE)
inspect(bread_rules)
plot(bread_rules, method="graph")
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