改用MyAnalyzer的KMeans聚类算法

<strong><span style="font-size:18px;">/*** * @author YangXin * @info 改用MyAnalyzer的KMeans聚类算法 */package unitTen;import java.io.File;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.SequenceFile;import org.apache.lucene.analysis.Analyzer;import org.apache.mahout.clustering.Cluster;import org.apache.mahout.clustering.kmeans.KMeansDriver;import org.apache.mahout.clustering.kmeans.RandomSeedGenerator;import org.apache.mahout.common.HadoopUtil;import org.apache.mahout.common.distance.CosineDistanceMeasure;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 = 99;    int maxNGramSize = 1;    int minLLRValue = 50;    int reduceTasks = 1;    int chunkSize = 200;    int norm = -1;    boolean sequentialAccessOutput = true;        String inputDir = "reuters-seqfiles";    File inputDirFile = new File(inputDir);    Configuration conf = new Configuration();    FileSystem fs = FileSystem.get(conf);    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 centroids = new Path(outputDir, "centroids");    Path clusterOutput = new Path(outputDir, "clusters");        RandomSeedGenerator.buildRandom(conf, vectorsFolder, centroids, 20,      new CosineDistanceMeasure());    KMeansDriver.run(conf, vectorsFolder, centroids, clusterOutput,      new CosineDistanceMeasure(), 0.01, 20, true, false);        SequenceFile.Reader reader = new SequenceFile.Reader(fs,        new Path(clusterOutput, Cluster.CLUSTERED_POINTS_DIR                                + "/part-m-00000"), conf);  }}</span></strong>