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

import weka.associations.FPGrowth; 

The FP-Growth algorithm 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.