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

java.lang.Object
  extended by de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling
      extended by de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogGammaScaling
All Implemented Interfaces:
InspectionUtilFrequentlyScanned, 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://www.dbs.ifi.lmu.de/~zimek/publications/SDM2011/SDM11-outlier-preprint.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
static class MinusLogGammaScaling.Parameterizer
          Parameterization class.
 
Field Summary
(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
MinusLogGammaScaling()
          Constructor.
 
Method Summary
 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
 
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 Database, Result or Annotation.

Specified by:
prepare in interface OutlierScalingFunction
Overrides:
prepare in class OutlierGammaScaling
Parameters:
or - Outlier result to use

Release 0.4.0 (2011-09-20_1324)