In this chapter, you learned how to leverage association rule learning on transactional datasets to gain insight about frequent patterns. We performed an affinity analysis in Weka and learned that the hard work lies in the analysis of results—careful attention is required when interpreting rules, as association (that is, correlation) is not the same as causation.
In the next chapter, we'll look at how to take the problem of item recommendation to the next level using a scalable machine-learning library, Apache Mahout, which is able to handle big data.
