数据格式：
1001,Choclates
1001,Pencil
1001,Marker
1002,Pencil
1002,Choclates
1003,Pencil
1003,Coke
1003,Eraser
1004,Pencil
1004,Choclates
1004,Cookies
1005,Marker
1006,Pencil
1006,Marker
1007,Pencil
1007,Choclates
R Source Code:
#Install the R package arules install.packages("arules"); #load the arules package library("arules"); # read the transaction file as a Transaction class # file – csv/txt # format – single/basket (For ‘basket’ format, each line in the transaction data file represents a transaction # where the items (item labels) are separated by the characters specified by sep. For ‘single’ format, # each line corresponds to a single item, containing at least ids for the transaction and the item. ) # rm.duplicates – TRUE/FALSE # cols - For the ‘single’ format, cols is a numeric vector of length two giving the numbers of the columns (fields) # with the transaction and item ids, respectively. For the ‘basket’ format, cols can be a numeric scalar # giving the number of the column (field) with the transaction ids. If cols = NULL # sep – “,” for csv, “ ” for tab delimited txn = read.transactions(file="D:\Transactions_sample.csv", rm.duplicates= FALSE, format="single",sep=",",cols =c(1,2)); # Run the apriori algorithm basket_rules <- apriori(txn,parameter = list(sup = 0.5, conf = 0.9,target="rules")); # Check the generated rules using inspect inspect(basket_rules); #If huge number of rules are generated specific rules can read using index inspect(basket_rules[1]); #To visualize the item frequency in txn file itemFrequencyPlot(txn); #To see how the transaction file is read into txn variable. inspect(txn); library(arulesViz) #arulesViz中有很多图形，介绍几个好看的，画图的对象都是rules plot(rules, shading="order", control=list(main = "Two-key plot")) plot(rules, method="grouped") plot(rules, method="graph")
参考文献：