de.lmu.ifi.dbs.elki.utilities.scaling.outlier

Class MinusLogGammaScaling

java.lang.Object

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogGammaScaling

All Implemented Interfaces:

Parameterizable, OutlierScalingFunction, ScalingFunction

@Reference(authors="H.-P. Kriegel, P. Kr\u00f6ger, E. Schubert, A. Zimek",
           title="Interpreting and Unifying Outlier Scores",
           booktitle="Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011",
           url="http://siam.omnibooksonline.com/2011datamining/data/papers/018.pdf")
public class MinusLogGammaScaling
extends OutlierGammaScaling

Scaling that can map arbitrary values to a probability in the range of [0:1], by assuming a Gamma distribution on the data and evaluating the Gamma CDF.

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
(package private) double	max Maximum value seen
(package private) double	mlogmax Minimum value (after log step, so maximum again)

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling

atmean, k, meta, normalize, NORMALIZE_ID, theta

Constructor Summary

Constructors

Constructor and Description
MinusLogGammaScaling() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	prepare(OutlierResult or) Prepare is called once for each data set, before getScaled() will be called.
protected double	preScale(double score) Normalize data if necessary.

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling

getMax, getMin, getScaled, prepare

Methods inherited from class java.lang.Object

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

Field Detail

max

double max

Maximum value seen

mlogmax

double mlogmax

Minimum value (after log step, so maximum again)

Constructor Detail

MinusLogGammaScaling

public MinusLogGammaScaling()

Constructor.

Method Detail

preScale

protected double preScale(double score)

Description copied from class: OutlierGammaScaling

Normalize data if necessary. Note: this is overridden by MinusLogGammaScaling!

Overrides:

preScale in class OutlierGammaScaling

Parameters:

score - Original score

Returns:

Normalized score.

prepare

public void prepare(OutlierResult or)

Description copied from interface: OutlierScalingFunction

Prepare is called once for each data set, before getScaled() will be called. This function can be used to extract global parameters such as means, minimums or maximums from the outlier scores.

Specified by:

prepare in interface OutlierScalingFunction

Overrides:

prepare in class OutlierGammaScaling

Parameters:

or - Outlier result to use