Machine Learning with Spark - Second Edition

FP-Growth Basic Sample

Let's start with a very simple dataset of random numbers:

val transactions = Seq(       "r z h k p",       "z y x w v u t s",       "s x o n r",       "x z y m t s q e",       "z",       "x z y r q t p")       .map(_.split(" "))

We will find out the most frequent items (character in this case). First, we will get the spark context as follows:

val sc = new SparkContext("local[2]", "Chapter 5 App")

Convert our data in an RDD:

val rdd = sc.parallelize(transactions, 2).cache()

Initialize the FPGrowth instance:

val fpg = new FPGrowth()

FP-Growth can be configured with the following parameters:

• minSupport: the minimum support number for an itemset to be identified as frequent. For example, if an item appears in 3 out of 10 transactions, it has a support ...