weka.classifiers.misc

Class LOF

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.misc.LOF

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class LOF
extends weka.classifiers.AbstractClassifier
implements java.io.Serializable, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.TechnicalInformationHandler, weka.core.RevisionHandler

A Classifier that applies the LOF (Local Outlier Factor) algorithm to compute an "outlier" score for each instance in the data. The data is expected to have a unary or binary class attribute, which is ignored at training time. The distributionForInstance() method returns 1 - normalized outlier score in the first element of the distribution. If the class attribute is binary, then the second element holds the normalized outlier score. To evaluate performance of this method for a dataset where outliers/anomalies are known, simply code the outliers using the class attribute: normal cases should correspond to the first value of the class attribute; outliers to the second one.

Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable.

For more information, see:

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

BibTeX:

 @article{Breunig2000,
    author = {Markus M. Breunig and Hans-Peter Kriegel and Raymond T. Ng and Jorg Sander},
    journal = {ACM SIGMOD Record},
    number = {2},
    pages = {93-104},
    publisher = {ACM New York},
    title = {LOF: Identifying Density-Based Local Outliers},
    volume = {29},
    year = {2000}
 }
 

Valid options are:

 -min <num>
  Lower bound on the k nearest neighbors for finding max LOF (minPtsLB)
  (default = 10)

 -max <num>
  Upper bound on the k nearest neighbors for finding max LOF (minPtsUB)
  (default = 40)

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).

Version:

$Revision: $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
LOF()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances data)
double[]	distributionForInstance(weka.core.Instance inst)
weka.core.Capabilities	getCapabilities() Returns the Capabilities of this filter.
java.lang.String	getMinPointsLowerBound() Get the lower bound (minPtsLB) to use on the range for k when determining the maximum LOF value
java.lang.String	getMinPointsUpperBound() Get the upper bound (minPtsUB) to use on the range for k when determining the maximum LOF value
weka.core.neighboursearch.NearestNeighbourSearch	getNNSearch() Get the nearest neighbor search method to use
java.lang.String	getNumExecutionSlots() Get the degree of parallelism to use.
java.lang.String[]	getOptions() Gets the current settings of the scheme.
java.lang.String	getRevision() Returns the revision string.
weka.core.TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
java.lang.String	globalInfo() Returns a string describing this scheme
java.util.Enumeration<weka.core.Option>	listOptions() Gets an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for this class.
java.lang.String	minPointsLowerBoundTipText() Returns the tip text for this property
java.lang.String	minPointsUpperBoundTipText() Returns the tip text for this property
java.lang.String	NNSearchTipText() Returns the tip text for this property
java.lang.String	numExecutionSlotsTipText() Returns the tip text for this property
void	setMinPointsLowerBound(java.lang.String pts) Set the lower bound (minPtsLB) to use on the range for k when determining the maximum LOF value
void	setMinPointsUpperBound(java.lang.String pts) Set the upper bound (minPtsUB) to use on the range for k when determining the maximum LOF value
void	setNNSearch(weka.core.neighboursearch.NearestNeighbourSearch s) Set the nearest neighbor search method to use
void	setNumExecutionSlots(java.lang.String slots) Set the degree of parallelism to use.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString()

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

LOF

public LOF()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this scheme

Returns:

a description of the scheme suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns the Capabilities of this filter.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this object

See Also:

Capabilities

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Gets an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.AbstractClassifier

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -min <num>
  Lower bound on the k nearest neighbors for finding max LOF (minPtsLB)
  (default = 10)

 -max <num>
  Upper bound on the k nearest neighbors for finding max LOF (minPtsUB)
  (default = 40)

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the scheme.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

minPointsLowerBoundTipText

public java.lang.String minPointsLowerBoundTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinPointsLowerBound

public void setMinPointsLowerBound(java.lang.String pts)

Set the lower bound (minPtsLB) to use on the range for k when determining the maximum LOF value

Parameters:

pts - the lower bound

getMinPointsLowerBound

public java.lang.String getMinPointsLowerBound()

Get the lower bound (minPtsLB) to use on the range for k when determining the maximum LOF value

Returns:

the lower bound

minPointsUpperBoundTipText

public java.lang.String minPointsUpperBoundTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinPointsUpperBound

public void setMinPointsUpperBound(java.lang.String pts)

Set the upper bound (minPtsUB) to use on the range for k when determining the maximum LOF value

Parameters:

pts - the upper bound

getMinPointsUpperBound

public java.lang.String getMinPointsUpperBound()

Get the upper bound (minPtsUB) to use on the range for k when determining the maximum LOF value

Returns:

the upper bound

NNSearchTipText

public java.lang.String NNSearchTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNNSearch

public void setNNSearch(weka.core.neighboursearch.NearestNeighbourSearch s)

Set the nearest neighbor search method to use

Parameters:

s - the nearest neighbor search method to use

getNNSearch

public weka.core.neighboursearch.NearestNeighbourSearch getNNSearch()

Get the nearest neighbor search method to use

Returns:

the nearest neighbor search method to use

numExecutionSlotsTipText

public java.lang.String numExecutionSlotsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumExecutionSlots

public void setNumExecutionSlots(java.lang.String slots)

Set the degree of parallelism to use.

Parameters:

slots - the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds

getNumExecutionSlots

public java.lang.String getNumExecutionSlots()

Get the degree of parallelism to use.

Returns:

the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds

buildClassifier

public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Throws:

java.lang.Exception

distributionForInstance

public double[] distributionForInstance(weka.core.Instance inst)
                                 throws java.lang.Exception

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Throws:

java.lang.Exception

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] args)

Main method for this class.

Parameters:

args - arguments to use