Learning association rules from data

Association rule learning is a machine-learning technique to discover associations and rules between various features or variables in a dataset. A similar technique in statistics is known as correlation, which is covered in Chapter 3, Analyzing Data Statistically, but association rule learning is more useful in decision making. For instance, by analyzing big supermarket data, a machine-learning learner can discover that if a person buys onions, tomatoes, chicken patty, and mayonnaise, she will most likely buy buns (to make burgers).

In this recipe, we will see how we can use Weka to learn association rules from datasets.

Getting ready

We will be using the supermarket dataset that can be found in the data directory of our installed Weka directory. The total number of instances in the dataset is 4,627 instances with 217 binary attributes each. The attributes have a value of true or missing. There is a nominal class attribute called total that has the value...