public static void main(String[] args) {
    // Set the logging level to statistics:
    LoggingConfiguration.setStatistics();

    // Generate a random data set.
    // Note: ELKI has a nice data generator class, use that instead.
    double[][] data = new double[1000][2];
    for(int i = 0; i < data.length; i++) {
      for(int j = 0; j < data[i].length; j++) {
        data[i][j] = Math.random();
      }
    }

    // Adapter to load data from an existing array.
    DatabaseConnection dbc = new ArrayAdapterDatabaseConnection(data);
    // Create a database (which may contain multiple relations!)
    Database db = new StaticArrayDatabase(dbc, null);
    // Load the data into the database (do NOT forget to initialize...)
    db.initialize();
    // Relation containing the number vectors:
    Relation<NumberVector> rel = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // We know that the ids must be a continuous range:
    DBIDRange ids = (DBIDRange) rel.getDBIDs();

    // K-means should be used with squared Euclidean (least squares):
    SquaredEuclideanDistanceFunction dist = SquaredEuclideanDistanceFunction.STATIC;
    // Default initialization, using global random:
    // To fix the random seed, use: new RandomFactory(seed);
    RandomUniformGeneratedInitialMeans init = new RandomUniformGeneratedInitialMeans(RandomFactory.DEFAULT);

    // Textbook k-means clustering:
    KMeansLloyd<NumberVector> km = new KMeansLloyd<>(dist, //
    3 /* k - number of partitions */, //
    0 /* maximum number of iterations: no limit */, init);

    // K-means will automatically choose a numerical relation from the data set:
    // But we could make it explicit (if there were more than one numeric
    // relation!): km.run(db, rel);
    Clustering<KMeansModel> c = km.run(db);

    // Output all clusters:
    int i = 0;
    for(Cluster<KMeansModel> clu : c.getAllClusters()) {
      // K-means will name all clusters "Cluster" in lack of noise support:
      System.out.println("#" + i + ": " + clu.getNameAutomatic());
      System.out.println("Size: " + clu.size());
      System.out.println("Center: " + clu.getModel().getPrototype().toString());
      // Iterate over objects:
      System.out.print("Objects: ");
      for(DBIDIter it = clu.getIDs().iter(); it.valid(); it.advance()) {
        // To get the vector use:
        // NumberVector v = rel.get(it);

        // Offset within our DBID range: "line number"
        final int offset = ids.getOffset(it);
        System.out.print(" " + offset);
        // Do NOT rely on using "internalGetIndex()" directly!
      }
      System.out.println();
      ++i;
    }
  }
public static void main ( String [ ] args ) { LoggingConfiguration . setStatistics ( ) ; double [ ] [ ] data = new double [ 1000 ] [ 2 ] ; for ( int i = 0 ; i < data . length ; i ++ ) { for ( int j = 0 ; j < data [ i ] . length ; j ++ ) { data [ i ] [ j ] = Math . random ( ) ; } } DatabaseConnection dbc = new ArrayAdapterDatabaseConnection ( data ) ; Database db = new StaticArrayDatabase ( dbc , null ) ; db . initialize ( ) ; Relation < NumberVector > rel = db . getRelation ( TypeUtil . NUMBER_VECTOR_FIELD ) ; DBIDRange ids = ( DBIDRange ) rel . getDBIDs ( ) ; SquaredEuclideanDistanceFunction dist = SquaredEuclideanDistanceFunction . STATIC ; RandomUniformGeneratedInitialMeans init = new RandomUniformGeneratedInitialMeans ( RandomFactory . DEFAULT ) ; KMeansLloyd < NumberVector > km = new KMeansLloyd < > ( dist , 3 , 0 , init ) ; Clustering < KMeansModel > c = km . run ( db ) ; int i = 0 ; for ( Cluster < KMeansModel > clu : c . getAllClusters ( ) ) { System . out . println ( "#" + i + ": " + clu . getNameAutomatic ( ) ) ; System . out . println ( "Size: " + clu . size ( ) ) ; System . out . println ( "Center: " + clu . getModel ( ) . getPrototype ( ) . toString ( ) ) ; System . out . print ( "Objects: " ) ; for ( DBIDIter it = clu . getIDs ( ) . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { final int offset = ids . getOffset ( it ) ; System . out . print ( " " + offset ) ; } System . out . println ( ) ; ++ i ; } }
Main method

@param args Command line parameters (not supported)