The goal of apriori is to compute the frequent itemsets and the association rules in an efficient way, as well as to compute support and confidence for these. Going into the details of these computations is beyond the scope of this chapter. In what follows, we briefly examine how itemset generation and rule generation are accomplished.
The most straightforward way to compute frequent itemsets would be to consider all the possible itemsets and discard those with support lower than minimal support. This is particularly inefficient, as generating itemsets and then discarding them is a waste of computation power. The goal is, of course, to generate only the itemsets that are useful for the analysis: those with support higher than minimal support. Let's continue with our previous example. The following table presents the same data using a binary representation:
|
Transaction |
Cherry Coke |
Chicken wings |
Chips |
Chocolate cake... |
|---|
