diff --git a/plotClassification.py b/plotClassification.py
new file mode 100755
index 0000000..3417f41
--- /dev/null
+++ b/plotClassification.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+
+FILENAME = sys.argv[1]  # CSV file
+
+data = np.loadtxt(FILENAME, delimiter=',')
+
+D = data[0].size - 1 # Number of dimensions
+
+assert D <= 2
+assert D > 0
+
+X = data[:, 0]
+Y = data[:, 1] if D > 1 else np.zeros(len(data))
+C = data[:, -1]
+
+plt.scatter(X, Y, c=C)
+plt.show()
diff --git a/src/main/java/it/polimi/middleware/projects/flink/KMeans.java b/src/main/java/it/polimi/middleware/projects/flink/KMeans.java
index 3e70451..5e0b010 100644
--- a/src/main/java/it/polimi/middleware/projects/flink/KMeans.java
+++ b/src/main/java/it/polimi/middleware/projects/flink/KMeans.java
@@ -1,11 +1,26 @@
 package it.polimi.middleware.projects.flink;
 
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Random;
+
 import org.apache.flink.api.common.functions.MapFunction;
 import org.apache.flink.api.common.functions.ReduceFunction;
+import org.apache.flink.api.common.functions.RichMapFunction;
 import org.apache.flink.api.common.typeinfo.TypeHint;
 import org.apache.flink.api.common.typeinfo.TypeInformation;
 import org.apache.flink.api.java.tuple.Tuple1;
 import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.configuration.Configuration;
 
 import org.apache.flink.api.java.DataSet;
 import org.apache.flink.api.java.ExecutionEnvironment;
@@ -18,6 +33,8 @@ public class KMeans {
         final ParameterTool params = ParameterTool.fromArgs(args);
         final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
 
+        final Integer k = params.getInt("k", 3);
+
         // Read CSV input
         DataSet<Tuple1<Double>> csvInput = env.readCsvFile(params.get("input")).types(Double.class);
 
@@ -25,26 +42,127 @@ public class KMeans {
         DataSet<Double> input = csvInput
             .map(point -> point.f0);
 
-        // DEBUG Means all the points
-        DataSet<Tuple1<Double>> mean = input
-            .map(new MapFunction<Double, Tuple2<Double, Integer>>() {
-                public Tuple2<Double, Integer> map(Double value) {
-                    return new Tuple2<Double, Integer>(value, 1);
-                }
-            })
-            .reduce(new ReduceFunction<Tuple2<Double, Integer>>() {
-                public Tuple2<Double, Integer> reduce(Tuple2<Double, Integer> a, Tuple2<Double, Integer> b) {
-                    return new Tuple2<Double, Integer>(a.f0 + b.f0, a.f1 + b.f1);
-                }
-            })
-            .map(new MapFunction<Tuple2<Double, Integer>, Tuple1<Double>>() {
-                public Tuple1<Double> map(Tuple2<Double, Integer> value) {
-                    return new Tuple1<Double>(value.f0 / value.f1);
-                }
-            });
+        // Generate random centroids
+        final RandomCentroids r = new RandomCentroids(k);
+        DataSet<Double> centroids = env.fromCollection(r, Double.class);
 
-        mean.writeAsCsv(params.get("output", "output.csv"));
+        centroids.print();
+
+        // Assign points to centroids
+        DataSet<Tuple2<Double, Integer>> assigned = input
+            .map(new AssignCentroid()).withBroadcastSet(centroids, "centroids");
+
+        // Calculate means
+        DataSet<Double> newCentroids = assigned
+            .map(new MeanPrepare())
+            .groupBy(1) // GroupBy CentroidID
+            .reduce(new MeanSum())
+            .map(new MeanDivide());
+
+        // Re-assign points to centroids
+        assigned = input
+            .map(new AssignCentroid()).withBroadcastSet(newCentroids, "centroids");
+
+        newCentroids.print();
+        assigned.writeAsCsv(params.get("output", "output.csv"));
 
         env.execute("K-Means clustering");
     }
+
+
+    public static class RandomCentroids implements Iterator<Double>, Serializable {
+
+        Integer k;
+        Integer i;
+        Random r;
+
+        public RandomCentroids(Integer k) {
+            this.k = k;
+            this.i = 0;
+            this.r = new Random(0);
+        }
+
+        @Override
+        public boolean hasNext() {
+            return i < k;
+        }
+
+        @Override
+        public Double next() {
+            i += 1;
+            return r.nextDouble();
+        }
+
+        private void readObject(ObjectInputStream inputStream) throws ClassNotFoundException, IOException {
+            inputStream.defaultReadObject();
+        }
+        private void writeObject(ObjectOutputStream outputStream) throws IOException {
+            outputStream.defaultWriteObject();
+        }
+    }
+
+    public static class AssignCentroid extends RichMapFunction<Double, Tuple2<Double, Integer>> {
+        // Point → Point, CentroidID
+        private List<Double> centroids;
+
+        @Override
+        public void open(Configuration parameters) throws Exception {
+            centroids = new ArrayList(getRuntimeContext().getBroadcastVariable("centroids"));
+            Collections.sort(centroids);
+        }
+
+        @Override
+        public Tuple2<Double, Integer> map(Double point) {
+            Integer c;
+            Double centroid;
+            Double distance;
+            Integer minCentroid = 4;
+            Double minDistance = Double.POSITIVE_INFINITY;
+
+            for (c = 0; c < centroids.size(); c++) {
+                centroid = centroids.get(c);
+                distance = distancePointCentroid(point, centroid);
+                if (distance < minDistance) {
+                    minCentroid = c;
+                    minDistance = distance;
+                }
+            }
+
+            return new Tuple2<Double, Integer>(point, minCentroid);
+        }
+
+        private Double distancePointCentroid(Double point, Double centroid) {
+            return Math.abs(point - centroid);
+            // return Math.sqrt(Math.pow(point, 2) + Math.pow(centroid, 2));
+        }
+    }
+
+    public static class MeanPrepare implements MapFunction<Tuple2<Double, Integer>, Tuple3<Double, Integer, Integer>> {
+        // Point, CentroidID → Point, CentroidID, Number of points
+        @Override
+        public Tuple3<Double, Integer, Integer> map(Tuple2<Double, Integer> point) {
+            return new Tuple3<Double, Integer, Integer>(point.f0, point.f1, 1);
+        }
+    }
+
+    public static class MeanSum implements ReduceFunction<Tuple3<Double, Integer, Integer>> {
+        // Point, CentroidID (irrelevant), Number of points
+        @Override
+        public Tuple3<Double, Integer, Integer> reduce(Tuple3<Double, Integer, Integer> a, Tuple3<Double, Integer, Integer> b) {
+            return new Tuple3<Double, Integer, Integer>(sumPoints(a.f0, b.f0), a.f1, a.f2 + b.f2);
+        }
+
+        private Double sumPoints(Double a, Double b) {
+            return a + b;
+        }
+    }
+
+    public static class MeanDivide implements MapFunction<Tuple3<Double, Integer, Integer>, Double> {
+        // Point, CentroidID (irrelevant), Number of points → Point
+        @Override
+        public Double map(Tuple3<Double, Integer, Integer> point) {
+            return point.f0 / point.f2;
+        }
+    }
+
 }