A parallel FP-growth algorithm to mine frequent itemsets.
ml_fpgrowth(x, items_col = "items", min_confidence = 0.8,
min_support = 0.3, prediction_col = "prediction",
uid = random_string("fpgrowth_"), ...)ml_association_rules(model)
ml_freq_itemsets(model)
A spark_connection, ml_pipeline, or a tbl_spark.
Items column name. Default: "items"
Minimal confidence for generating Association Rule.
min_confidence will not affect the mining for frequent itemsets, but
will affect the association rules generation. Default: 0.8
Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (min_support * size-of-the-dataset) times will be output in the frequent itemsets. Default: 0.3
Prediction column name.
A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
A fitted FPGrowth model returned by ml_fpgrowth()