Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.WeightedPearsonCorrelationDistanceFunction

de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.WeightedPearsonCorrelationDistanceFunction

Pearson correlation distance function for feature vectors. The Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-r. Hence, possible values of this distance are between 0 and 2. The distance between two vectors will be low (near 0), if their attribute values are dimension-wise strictly positively correlated, it will be high (near 2), if their attribute values are dimension-wise strictly negatively correlated. For Features with uncorrelated attributes, the distance value will be intermediate (around 1). This variation is for weighted dimensions. @author Arthur Zimek @author Erich Schubert

  }


  private PrimitiveDoubleDistanceFunction<NumberVector<?, ?>> getDistanceFunction(double[] estimated_weights) {
    // return new WeightedSquaredEuclideanDistanceFunction(estimated_weights);
    // return new WeightedLPNormDistanceFunction(1.0, estimated_weights);
    return new WeightedPearsonCorrelationDistanceFunction(estimated_weights);
  }

Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.WeightedPearsonCorrelationDistanceFunction

Related Classes of de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.WeightedPearsonCorrelationDistanceFunction