--- layout: global title: Frequent Pattern Mining - MLlib displayTitle: MLlib - Frequent Pattern Mining --- Mining frequent items, itemsets, subsequences, or other substructures is usually among the first steps to analyze a large-scale dataset, which has been an active research topic in data mining for years. We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning) for more information. MLlib provides a parallel implementation of FP-growth, a popular algorithm to mining frequent itemsets. ## FP-growth The FP-growth algorithm is described in the paper [Han et al., Mining frequent patterns without candidate generation](http://dx.doi.org/10.1145/335191.335372), where "FP" stands for frequent pattern. Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items. Different from [Apriori-like](http://en.wikipedia.org/wiki/Apriori_algorithm) algorithms designed for the same purpose, the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets explicitly, which are usually expensive to generate. After the second step, the frequent itemsets can be extracted from the FP-tree. In MLlib, we implemented a parallel version of FP-growth called PFP, as described in [Li et al., PFP: Parallel FP-growth for query recommendation](http://dx.doi.org/10.1145/1454008.1454027). PFP distributes the work of growing FP-trees based on the suffices of transactions, and hence more scalable than a single-machine implementation. We refer users to the papers for more details. MLlib's FP-growth implementation takes the following (hyper-)parameters: * `minSupport`: the minimum support for an itemset to be identified as frequent. For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6. * `numPartitions`: the number of partitions used to distribute the work. **Examples**