Association Rules - Data Mining for the Masses - page 88

confidence percentages are calculated and about the importance of these two metrics in identifying
rules and determining their strength in a data set.
REVIEW QUESTIONS
1) What are association rules? What are they good for?
2) What are the two main metrics that are calculated in association rules and how are they
calculated?
3) What data type must a data set's attributes be in order to use Frequent Pattern operators in
RapidMiner?
4) How are rule results interpreted? In this chapter's example, what was our strongest rule?
How do we know?
EXERCISE
In explaining support and confidence percentages in this chapter, the classic example of shopping
basket analysis was used. For this exercise, you will do a shopping basket association rule analysis.
Complete the following steps:
1) Using the Internet, locate a sample shopping basket data set. Search terms such as
'association rule data set' or 'shopping basket data set' will yield a number of downloadable
examples. With a little effort, you will be able to find a suitable example.
2) If necessary, convert your data set to CSV format and import it into your RapidMiner
repository. Give it a descriptive name and drag it into a new process window.
3) As necessary, conduct your Data Understanding and Data Preparation activities on your
data set. Ensure that all of your variables have consistent data and that their data types are
appropriate for the FP-Growth operator.