Objectives
The objectives of this section are:
to explain the problem of rule generation
to define confidence-based pruning
to show how rule generation works in the Apriori Algorithm
Outcomes
By the time you have completed this section you will be able to:
generate rules based on frequent itemset
use the Confidence theorem presented to prune the rule set.
explain how rule generation occurs in the Apriori Algorithm
Rule Generation
Midpoint Pacemaker
Before we move forward let’s do a quick recap.
If an itemset has a support count of 3 and the minsup is 4 and minconf of 3 can this item be consider a frequent itemset?
What is the Apriori Algorithm and why is it important for generating association rules?
Why is the minimum confidence threshold important and how does it help us when distinguishing between association rules?
Hopefully these questions did not require that much thought because at this stage you should be familiar with itemsets and how to find the frequent itemset using the Apriori Algorithm. It doesn’t end here because not all frequent itemsets are strong association rules. We know that frequent itemsets have passed the minimum support but we don’t know how about the reliability of the rules that currently exist. In this section we generate candidate association rules from the frequent itemset and then determine which ones are strong association rules based on whether they pass the confidence test.
Rule Generation Procedure
There are two main steps the first is extraction and the second is calculation. The video clip below explains both of these steps in great detail.
