Database Reference
In-Depth Information
4)
Generate association rules for your data set. Modify your confidence and support values in
order to identify their most ideal levels such that you will have some interesting rules with
reasonable confidence and support. Look at the other measures of rule strength such as
LaPlace or Conviction.
5)
Document your findings. What rules did you find? What attributes are most strongly
associated with one another. Are there products that are frequently connected that
surprise you? Why do you think this might be? How much did you have to test different
support and confidence values before you found some association rules? Were any of your
association rules good enough that you would base decisions on them? Why or why not?
Challenge Step!
6)
Build a new association rule model using your same data set, but this time, use the W-
FPGrowth operator. (Hints for using the W-FPGrowth operator: (1) This operator creates
its own rules without help from other operators; and (2) This operator's support and
confidence parameters are labeled U and C, respectively.
Exploration!
7)
The Apriori algorithm is often used in data mining for associations. Search the
RapidMiner Operators tree for Apriori operators and add them to your data set in a new
process. Use the Help tab in RapidMiner's lower right hand corner to learn about these
operators' parameters and functions (be sure you have the operator selected in your main
process window in order to see its help content).
