In this chapter, we have studied association rules. We also discussed the Apriori algorithm, which is used for mining frequent itemsets to derive various association rules. We also learned about frequent pattern growth (FP-growth), which is similar to Apriori and about the frequent itemset generation technique, which is similar to the Apriori algorithm. Finally, we saw how FP-growth tends to have an edge over Apriori, as it is faster and more efficient, using an example.
In the next chapter, we will study probabilistic graphical models. We will learn in depth about the Bayesian rules and Bayesian networks.