de.lmu.ifi.dbs.elki.distance.distancefunction.correlation
Class PearsonCorrelationDistanceFunction<V extends NumberVector<V,N>,N extends Number>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction<O,D>
          de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction<V,DoubleDistance>
              de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.PearsonCorrelationDistanceFunction<V,N>

Type Parameters:

V - the type of FeatureVector to compute the distances in between

N - the type of Number of the attributes of vectors of type V

All Implemented Interfaces:

DistanceFunction<V,DoubleDistance>, MeasurementFunction<V,DoubleDistance>, Parameterizable

public class PearsonCorrelationDistanceFunction<V extends NumberVector<V,N>,N extends Number>
extends AbstractDistanceFunction<V,DoubleDistance>
implements Parameterizable

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).

Author:

Arthur Zimek

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

distanceFactory

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

PearsonCorrelationDistanceFunction()
Provides a PearsonCorrelationDistanceFunction.

Method Summary

DoubleDistance

distance(V v1, V v2) Computes the Pearson correlation distance for two given feature vectors.

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction

distance, distance

Methods inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

getDatabase, getDistanceFactory, infiniteDistance, nullDistance, setDatabase, undefinedDistance, valueOf

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.MeasurementFunction

getDistanceFactory, infiniteDistance, nullDistance, setDatabase, undefinedDistance, valueOf

Constructor Detail

PearsonCorrelationDistanceFunction

public PearsonCorrelationDistanceFunction()

Provides a PearsonCorrelationDistanceFunction.

Method Detail

distance

public DoubleDistance distance(V v1,
                               V v2)

Computes the Pearson correlation distance for two given 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.

Specified by:

distance in interface DistanceFunction<V extends NumberVector<V,N>,DoubleDistance>

Parameters:

v1 - first feature vector

v2 - second feature vector

Returns:

the Pearson correlation distance for two given feature vectors v1 and v2