参考资料链接：https://github.com/CraigCovey/spark-examples/blob/f8182a6736fd5293dfa03b023eb1423363ba6041/spark-1_6/scala/clustering/kmeans/kmeans_clustering_main.scala

package com.xx.Kmeans_sampleimport org.apache.spark.SparkConfimport org.apache.spark.sql.SparkSessionimport org.apache.spark.ml.feature.{StandardScaler, VectorAssembler}import org.apache.spark.ml.Pipelineimport org.apache.spark.ml.clustering.{KMeans, KMeansModel}object KmeansClusteringMain {  def main(args: Array[String]): Unit = {    val sparkConf = new SparkConf().setAppName("ReadData").setMaster("local").set("spark.sql.warehouse.dir", "file:///C:/Users/username/IdeaProjects/spark_demo/spark-warehouse")    val sparkSession = SparkSession.builder().config(sparkConf).getOrCreate()    val input_path = "D:/spark_data/iris.csv"    val data = sparkSession.sqlContext.read.format("csv").option("sep", ",")      .option("inferSchema", "true")      .option("header", "true")      .load(input_path)    val predictorVariables : Array[String] = Array("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")    val assembler = new VectorAssembler()      .setInputCols(predictorVariables)      .setOutputCol("clusteringFeatures")    val scaler = new StandardScaler()      .setInputCol("clusteringFeatures")      .setOutputCol("scaledClusteringFeatures")      .setWithMean(true)      .setWithStd(true)    val kmeansAlgorithm = new KMeans()      .setK(10)                   // <-- number of clusters      .setSeed(1024)      .setMaxIter(20)                 // <-- hyperparameter      .setTol(1.0e-05)                // <-- hyperparameter      .setFeaturesCol("scaledClusteringFeatures")      .setPredictionCol("columnCategory")   // <-- create your own column name    val pipeline = new Pipeline().setStages(Array(assembler, scaler, kmeansAlgorithm))    // Train model    val pipelineModel = pipeline.fit(data)    // Apply model to dataframe    val kmeansPrediction = pipelineModel.transform(data)    kmeansPrediction.show()    // Evaluate clustering by computing Within Set Sum of Squared Errors    val kmeansModel = pipelineModel.stages.last.asInstanceOf[KMeansModel]    val cost = kmeansModel.computeCost(kmeansPrediction)    println("Clustering Cost: " + cost)    // Print cluster centers    val centers = kmeansModel.clusterCenters    println("Cluster Centers:")    centers.foreach(println)  }}