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package mllib.associationrule import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} import scala.io.{BufferedSource, Source} /** * created by LMR on 2019/6/11 */ object FPGrowthTest { def main(args: Array[String]): Unit = { val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("svm") val sc = new SparkContext(conf) //从windows本地读取数据，转化为RDD[Vector] val source: BufferedSource = Source.fromFile("E:\\IDEAWorkPlace\\SparkTest\\src\\main\\scala\\mllib\\data\\sample_fpgrowth.txt") val lines: Array[String] = source.getLines().toArray val data: RDD[String] = sc.parallelize(lines) val examples: RDD[Array[String]] = data.map(_.split(" ")).cache() val miniSupport = 0.2 val numPartition = 10 val model: FPGrowthModel[String] = new FPGrowth() .setMinSupport(miniSupport) .setNumPartitions(numPartition) .run(examples) println(s"Number of frequent itemsets: ${model.freqItemsets.count()}") val array: Array[FPGrowth.FreqItemset[String]] = model.freqItemsets.collect() model.freqItemsets.collect().foreach(itemset => println(itemset.items.mkString("[", ",", "]") + "," + itemset.freq)) } } 完整数据/代码地址：git地址
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