This node uses Spark MLlib to compute frequent item sets. See the Spark Association Rule Learner node to generate frequent item sets and association rules in one step.

Frequent item sets are computed using the FP-growth implementation provided by Spark MLlib, using input data with a collection column, where each cell holds the items of a transaction. Rows with missing values in the selected item column are ignored . FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets explicitly and then extracts the frequent item sets from this FP-tree. This approach avoids the usually expensive generation of explicit candidates sets used in Apriori-like algorithms designed for the same purpose. More information about the FP-Growth algorithm can be found in Han et al., Mining frequent patterns without candidate generation . Spark implements Parallel FP-growth (PFP) described in Li et al., PFP: Parallel FP-Growth for Query Recommendation .

Transactions/item sets are represented as collection columns. The Spark GroupBy or Spark SQL nodes are recommended to create collection columns in Spark.

See Association rule learning (Wikipedia) for general information.

This node requires at least Apache Spark 2.0.