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copied!<p>The given code is misleading, the code</p> <pre><code>Cluster cluster = new Cluster(vec, i, new EuclideanDistanceMeasure()); writer.append(new Text(cluster.getIdentifier()), cluster); } writer.close(); KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"), new Path("output"), new EuclideanDistanceMeasure(), 0.001, 10, true, false); SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("output/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-00000"), conf); </code></pre> <p>should be replaced by</p> <pre><code>Kluster cluster = new Kluster(vec, i, new EuclideanDistanceMeasure()); writer.append(new Text(cluster.getIdentifier()), cluster); } writer.close(); KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"), new Path("output"), new EuclideanDistanceMeasure(), 0.001, 10, true, false); SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("output/" + Kluster.CLUSTERED_POINTS_DIR + "/part-m-00000"), conf); </code></pre> <p>Cluster is an interface whereas <a href="https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/clustering/kmeans/Kluster.html" rel="nofollow">Kluster</a> is a class. Please check <a href="https://builds.apache.org/job/Mahout-Quality/javadoc/overview-summary.html" rel="nofollow">Mahout API Javadoc</a> for more information.</p> <p>To run kmeans with csv file, first you have to create a SequenceFile to pass as an argument in KmeansDriver. The following code reads each line of the CSV file "points.csv" and converts it into vector and write it to the SequenceFile "points.seq"</p> <pre><code>try ( BufferedReader reader = new BufferedReader(new FileReader("testdata2/points.csv")); SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,new Path("testdata2/points.seq"), LongWritable.class, VectorWritable.class) ) { String line; long counter = 0; while ((line = reader.readLine()) != null) { String[] c = line.split(","); if(c.length>1){ double[] d = new double[c.length]; for (int i = 0; i < c.length; i++) d[i] = Double.parseDouble(c[i]); Vector vec = new RandomAccessSparseVector(c.length); vec.assign(d); VectorWritable writable = new VectorWritable(); writable.set(vec); writer.append(new LongWritable(counter++), writable); } } writer.close(); } </code></pre> <p>Hope it helps!!</p>

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