Database Reference
In-Depth Information
[1] "whole milk, cereals"
[2] "tropical fruit, other vegetables, white bread,
bottled water, chocolate"
[3] "citrus fruit, tropical fruit, whole milk, butter,
curd, yogurt, flour, bottled water, dishes"
[4] "beef"
[5] "frankfurter, rolls/buns, soda"
[6] "chicken, tropical fruit"
[7] "butter, sugar, fruit/vegetable juice, newspapers"
[8] "fruit/vegetable juice"
[9] "packaged fruit/vegetables"
[10] "chocolate"
[11] "specialty bar"
The next section shows how to generate frequent itemsets from the
Groceries
dataset.
5.5.2 Frequent Itemset Generation
The
apriori()
function from the
arule
package implements the Apriori
algorithm to create frequent itemsets. Note that, by default, the
apriori()
function executes all the iterations at once. However, to illustrate how the Apriori
algorithm works, the code examples in this section manually set the parameters of
the
apriori()
function to simulate each iteration of the algorithm.
Assume that the minimum support threshold is set to 0.02 based on management
discretion. Because the dataset contains 9,853 transactions, an itemset should
appear at least 198 times to be considered a frequent itemset. The first iteration
of the Apriori algorithm computes the support of each product in the dataset
and retains those products that satisfy the minimum support. The following code
identifies 59 frequent 1-itemsets that satisfy the minimum support. The
parameters of
apriori()
specify the minimum and maximum lengths of the
itemsets, the minimum support threshold, and the target indicating the type of
association mined.
itemsets <- apriori(Groceries, parameter=list(minlen=1,
maxlen=1,
support=0.02, target="frequent itemsets"))
parameter specification:
