Critical to the understanding of MBA are the concepts of support, confidence, and lift. These are the measures that evaluated the goodness of fit for a set of association rules. You will also learn some specific definitions that are used in MBA, such as consequence, antecedent, and itemsets.
To introduce these concepts, we will first illustrate these terms through a very simplistic example. We will use only the first 10 transactions contained in the Groceries transaction file, which is contained in the arules package:
library(arules)
After the arules library is loaded, you can see a short description of the Groceries dataset by entering ?Groceries at the command line. The following description appears in the help window:
"The Groceries data set contains 1 month (30 days) of real-world point-of-sale transaction data from a typical local grocery outlet. The data set contains 9835 transactions and the items are aggregated to 169 categories".
For more information...
