Frequent Pattern Mining – FPGrowth — ml

A parallel FP-growth algorithm to mine frequent itemsets.

Usage

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)

Arguments

x: A spark_connection, ml_pipeline, or a tbl_spark.
items_col: Items column name. Default: "items"
min_confidence: 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
min_support: 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_col: Prediction column name.
uid: A character string used to uniquely identify the ML estimator.
...: Optional arguments; currently unused.
model: A fitted FPGrowth model returned by ml_fpgrowth()