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

Class PearsonCorrelationDistanceFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction

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

All Implemented Interfaces:

DistanceFunction<NumberVector>, NumberVectorDistanceFunction<NumberVector>, PrimitiveDistanceFunction<NumberVector>

public class PearsonCorrelationDistanceFunction
extends AbstractNumberVectorDistanceFunction

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).

Since:

0.3

Author:

Arthur Zimek

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PearsonCorrelationDistanceFunction.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
static PearsonCorrelationDistanceFunction	STATIC Static instance.

Constructor Summary

Constructors

Constructor and Description
PearsonCorrelationDistanceFunction() Deprecated. Use static instance!

Method Summary

Modifier and Type	Method and Description
double	distance(NumberVector v1, NumberVector v2) Computes the Pearson correlation distance for two given feature vectors.
boolean	equals(java.lang.Object obj)
int	hashCode()
java.lang.String	toString()

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

dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, getInputTypeRestriction

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction

instantiate

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction

isMetric, isSquared, isSymmetric

Field Detail

STATIC

public static final PearsonCorrelationDistanceFunction STATIC

Static instance.

Constructor Detail

PearsonCorrelationDistanceFunction

@Deprecated
public PearsonCorrelationDistanceFunction()

Deprecated. Use static instance!

Constructor - use STATIC instead.

Method Detail

distance

public double distance(NumberVector v1,
                       NumberVector 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.

Parameters:

v1 - first feature vector

v2 - second feature vector

Returns:

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

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object