package com.data.big.mlib; import org.apache.spark.api.java.*; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.clustering.KMeans; import org.apache.spark.mllib.clustering.KMeansModel; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.SparkConf; public class KMeansClusteringMlib { public static void main( String[] args ){ SparkConf conf = new SparkConf().setMaster("local[4]").setAppName("K-means Example"); JavaSparkContext sc = new JavaSparkContext(conf); // Load and parse data String path = "data/km-data.txt"; JavaRDD<String> data = sc.textFile(path); JavaRDD<Vector> parsedData = data.map( new Function<String, Vector>() { public Vector call(String s) { String[] sarray = s.split(" "); double[] values = new double[sarray.length]; for (int i = 0; i < sarray.length; i++) values[i] = Double.parseDouble(sarray[i]); return Vectors.dense(values); } } ); parsedData.cache(); // Cluster the data into two classes using KMeans int numClusters = 2; int numIterations = 20; KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations); // Evaluate clustering by computing Within Set Sum of Squared Errors double WSSSE = clusters.computeCost(parsedData.rdd()); System.out.println("Within Set Sum of Squared Errors = " + WSSSE); } }