We will use the Apriori algorithm as implemented in Weka. It iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence:

import java.io.BufferedReader;
import java.io.FileReader;
import weka.core.Instances;
import weka.associations.Apriori;

First, we will load the supermarket dataset:

Instances data = new Instances(
new BufferedReader(
new FileReader("datasets/chap5/supermarket.arff")));

Next, we will initialize an Apriori instance and call the buildAssociations(Instances) function to start frequent pattern mining, as follows:

Apriori model = new Apriori();
model.buildAssociations(data);

Finally, we can output the discovered itemsets and rules, as shown in the following code:

System.out.println(model);

The output is as follows:

Apriori
=======

Minimum support: 0.15 (694 instances)
Minimum metric <confidence>: 0.9
Number of cycles performed: 17

Generated sets of large itemsets:
Size of set of large itemsets L(1): 44
Size of set of large itemsets L(2): 380
Size of set of large itemsets L(3): 910
Size of set of large itemsets L(4): 633
Size of set of large itemsets L(5): 105
Size of set of large itemsets L(6): 1

Best rules found:

 1. biscuits=t frozen foods=t fruit=t total=high 788 ==> bread and cake=t 723    <conf:(0.92)> lift:(1.27) lev:(0.03) [155] conv:(3.35)
 2. baking needs=t biscuits=t fruit=t total=high 760 ==> bread and cake=t 696    <conf:(0.92)> lift:(1.27) lev:(0.03) [149] conv:(3.28)
 3. baking needs=t frozen foods=t fruit=t total=high 770 ==> bread and cake=t 705    <conf:(0.92)> lift:(1.27) lev:(0.03) [150] conv:(3.27)
...

The algorithm outputs ten best rules according to confidence. Let's look the first rule and interpret the output, as follows:

biscuits=t frozen foods=t fruit=t total=high 788 ==> bread and cake=t 723    <conf:(0.92)> lift:(1.27) lev:(0.03) [155] conv:(3.35)

It says that when biscuits, frozen foods, and fruits are bought together and the total purchase price is high, it is also very likely that bread and cake are purchased as well. The {biscuits, frozen foods, fruit, total high} itemset appears in 788 transactions, while the {bread, cake} itemset appears in 723 transactions. The confidence of this rule is 0.92, meaning that the rule holds true in 92% of transactions where the {biscuits, frozen foods, fruit, total high} itemset is present.

The output also reports additional measures such as lift, leverage, and conviction, which estimate the accuracy against our initial assumptions, for example, the 3.35 conviction value indicates that the rule would be incorrect 3.35 times as often if the association was purely a random chance. Lift measures the number of times X and Y occur together than expected if they were statistically independent (lift=1). The 2.16 lift in the X -> Y rule means that the probability of X is 2.16 times greater than the probability of Y.

Now, let's try to get the same results with more efficient FP-growth algorithm. FP-growth is also implemented in the weka.associations package:

import weka.associations.FPGrowth;

The FP-growth is initialized similarly as we did earlier:

FPGrowth fpgModel = new FPGrowth();
fpgModel.buildAssociations(data);
System.out.println(fpgModel);

The output reveals that FP-growth discovered 16 rules:

FPGrowth found 16 rules (displaying top 10)

 1. [fruit=t, frozen foods=t, biscuits=t, total=high]: 788 ==> [bread and cake=t]: 723   <conf:(0.92)> lift:(1.27) lev:(0.03) conv:(3.35) 
 2. [fruit=t, baking needs=t, biscuits=t, total=high]: 760 ==> [bread and cake=t]: 696   <conf:(0.92)> lift:(1.27) lev:(0.03) conv:(3.28) 
...

We can observe that FP-growth found the same set of rules as Apriori; however, the time required to process larger datasets can be significantly shorter.