spark版本 1.3.1

scala系統環境2.10.4  程式編譯版本2.11.8

需要注意的是，訓練資料和待測試資料都要是浮點型的，如果是int型的資料的話會報錯

package Kmeans
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.{SparkContext,SparkConf}
object KmeansTest {
  var ClusterIndex=0
def main(args:Array[String
 
]): Unit ={
    if(args.length<1){
      System.err.println("Usage:<dir:trainning data> <dir:test data>")
      System.exit(1)
    }

    val conf=new SparkConf()
    val sc=new SparkContext(conf)
    val data=sc.textFile(args(0))
    // the problem solved ,the data must be double type,not Int type
 
val parseddata=data.map(s=>{Vectors.dense(s.split("\t").map(_.trim).filter(!"".equals(_)).map(_.toDouble))}).cache()
    val numIterators=20
val numCluster=2
//trainning data
val rdd = sc.makeRDD(Array(Array(1.0,10.1,2.5),Array(2.0,5.2,3.8),Array(2.1,4.0,4.4),Array(0.6,2.0,3.1)))
    val data1 = rdd.map(f=>Vectors.dense
 
(f))

    val clusters=KMeans.train(data1,numCluster,numIterators)

    clusters.clusterCenters.foreach(x=>{println("Center index of cluster is :"+ClusterIndex+":")
    println(x)
    ClusterIndex+=1
})

   // System.exit(1)
parseddata.collect().foreach(dataline=>{
      val clusterIndex=clusters.predict(dataline)
      println("The data"+dataline+"belongs to cluster"+clusterIndex)
    })
    println("Kmeans cluster finished")

    sc.stop()
  }

}