/*
* Source code for Listing 9.4
*
*/
package mia.clustering.ch09;
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.SequenceFile;
import org.apache.lucene.analysis.Analyzer;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.WeightedVectorWritable;
import org.apache.mahout.clustering.canopy.CanopyDriver;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.common.distance.TanimotoDistanceMeasure;
import org.apache.mahout.vectorizer.DictionaryVectorizer;
import org.apache.mahout.vectorizer.DocumentProcessor;
import org.apache.mahout.vectorizer.tfidf.TFIDFConverter;
public class NewsKMeansClustering {
public static void main(String args[]) throws Exception {
int minSupport = 5;
int minDf = 5;
int maxDFPercent = 95;
int maxNGramSize = 2;
int minLLRValue = 50;
int reduceTasks = 1;
int chunkSize = 200;
int norm = 2;
boolean sequentialAccessOutput = true;
String inputDir = "inputDir";
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
/*
* SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, new Path(inputDir, "documents.seq"),
* Text.class, Text.class); for (Document d : Database) { writer.append(new Text(d.getID()), new
* Text(d.contents())); } writer.close();
*/
String outputDir = "newsClusters";
HadoopUtil.delete(conf, new Path(outputDir));
Path tokenizedPath = new Path(outputDir,
DocumentProcessor.TOKENIZED_DOCUMENT_OUTPUT_FOLDER);
MyAnalyzer analyzer = new MyAnalyzer();
DocumentProcessor.tokenizeDocuments(new Path(inputDir), analyzer.getClass()
.asSubclass(Analyzer.class), tokenizedPath, conf);
DictionaryVectorizer.createTermFrequencyVectors(tokenizedPath,
new Path(outputDir), conf, minSupport, maxNGramSize, minLLRValue, 2, true, reduceTasks,
chunkSize, sequentialAccessOutput, false);
TFIDFConverter.processTfIdf(
new Path(outputDir , DictionaryVectorizer.DOCUMENT_VECTOR_OUTPUT_FOLDER),
new Path(outputDir), conf, chunkSize, minDf,
maxDFPercent, norm, true, sequentialAccessOutput, false, reduceTasks);
Path vectorsFolder = new Path(outputDir, "tfidf-vectors");
Path canopyCentroids = new Path(outputDir , "canopy-centroids");
Path clusterOutput = new Path(outputDir , "clusters");
CanopyDriver.run(vectorsFolder, canopyCentroids,
new EuclideanDistanceMeasure(), 250, 120, false, false);
KMeansDriver.run(conf, vectorsFolder, new Path(canopyCentroids, "clusters-0"),
clusterOutput, new TanimotoDistanceMeasure(), 0.01,
20, true, false);
SequenceFile.Reader reader = new SequenceFile.Reader(fs,
new Path(clusterOutput + Cluster.CLUSTERED_POINTS_DIR + "/part-00000"), conf);
IntWritable key = new IntWritable();
WeightedVectorWritable value = new WeightedVectorWritable();
while (reader.next(key, value)) {
System.out.println(key.toString() + " belongs to cluster "
+ value.toString());
}
reader.close();
}
}