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POCannot Instantiate The type Cluster, KMean clustering Example in Mahout
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  1. POCannot Instantiate The type Cluster, KMean clustering Example in Mahout
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    copied!<p>Hi i was trying to run KmeanClustering Example in Mahout, getting stucked with an error in the sample Code. I'm getting error in the the below code snipet</p> <p><strong>Cluster cluster = new Cluster(vec, i, new EuclideanDistanceMeasure());</strong> </p> <p>It gives an error </p> <blockquote> <p>Cannot instantiate the Type Cluster</p> </blockquote> <p>(which is an Interface, my understanding).I want to run kmeans on My sample dataSet, Can anyone guide me in that too.</p> <p>I have Included The following Jars in my EClipse IDE</p> <p>mahout-math-0.7-cdh4.3.0.jar</p> <p>hadoop-common-2.0.0-cdh4.2.1.jar</p> <p>hadoop-hdfs-2.0.0-cdh4.2.1.jar</p> <p>hadoop-mapreduce-client-core-2.0.0-cdh4.2.1.jar</p> <p>mahout-core-0.7-cdh4.3.0.jar</p> <p>Check if i'm missing any essential jar, I will be running this On Hadoop CDH4.2.1</p> <p>Here attaching my whole Code, taken from <a href="https://github.com/tdunning/MiA/blob/master/src/main/java/mia/clustering/ch07/SimpleKMeansClustering.java" rel="nofollow">Github</a></p> <pre><code>package tryout; import java.io.File; import java.io.IOException; import java.util.ArrayList; import java.util.List; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.SequenceFile; import org.apache.hadoop.io.Text; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import org.apache.mahout.clustering.Cluster; import org.apache.mahout.clustering.classify.WeightedVectorWritable; import org.apache.mahout.clustering.kmeans.KMeansDriver; import org.apache.mahout.common.distance.EuclideanDistanceMeasure; public class SimpleKMeansClustering { public static final double[][] points = { {1, 1}, {2, 1}, {1, 2}, {2, 2}, {3, 3}, {8, 8}, {9, 8}, {8, 9}, {9, 9}}; public static void writePointsToFile(List&lt;Vector&gt; points, String fileName,FileSystem fs,Configuration conf) throws IOException { Path path = new Path(fileName); SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,path, LongWritable.class, VectorWritable.class); long recNum = 0; VectorWritable vec = new VectorWritable(); for (Vector point : points) { vec.set(point); writer.append(new LongWritable(recNum++), vec); } writer.close(); } public static List&lt;Vector&gt; getPoints(double[][] raw) { List&lt;Vector&gt; points = new ArrayList&lt;Vector&gt;(); for (int i = 0; i &lt; raw.length; i++) { double[] fr = raw[i]; Vector vec = new RandomAccessSparseVector(fr.length); vec.assign(fr); points.add(vec); } return points; } public static void main(String args[]) throws Exception { int k = 2; List&lt;Vector&gt; vectors = getPoints(points); File testData = new File("testdata"); if (!testData.exists()) { testData.mkdir(); } testData = new File("testdata/points"); if (!testData.exists()) { testData.mkdir(); } Configuration conf = new Configuration(); FileSystem fs = FileSystem.get(conf); writePointsToFile(vectors, "testdata/points/file1", fs, conf); Path path = new Path("testdata/clusters/part-00000"); SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,path, Text.class, Cluster.class); for (int i = 0; i &lt; k; i++) { Vector vec = vectors.get(i); 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); IntWritable key = new IntWritable(); WeightedVectorWritable value = new WeightedVectorWritable(); while (reader.next(key, value)) { System.out.println(value.toString() + " belongs to cluster " + key.toString()); } reader.close(); } } </code></pre> <p>Also guide me that, if i have my own dataset how to approach for that.</p>
 

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