We will apply the FP-Growth algorithm to find frequently recommended movies.
The FP-Growth algorithm has been described in the paper by Han et al., Mining frequent patterns without candidate generation available at: http://dx.doi.org/10.1145/335191.335372, where FP stands for the frequent pattern. For given a dataset of transactions, the first step of FP-Growth is to calculate item frequencies and identify frequent items. The second step of FP-Growth algorithm implementation uses a suffix tree (FP-tree) structure to encode transactions; this is done without generating candidate sets explicitly, which are usually expensive to generate for large datasets.
