weka.associations

Class PriorEstimation

java.lang.Object

weka.associations.PriorEstimation

All Implemented Interfaces:

java.io.Serializable, RevisionHandler

public class PriorEstimation
extends java.lang.Object
implements java.io.Serializable, RevisionHandler

Class implementing the prior estimattion of the predictive apriori algorithm for mining association rules. Reference: T. Scheffer (2001). Finding Association Rules That Trade Support Optimally against Confidence. Proc of the 5th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), pp. 424-435. Freiburg, Germany: Springer-Verlag.

Version:

$Revision: 1.7 $

Author:

Stefan Mutter (mutter@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PriorEstimation(Instances instances, int numRules, int numIntervals, boolean car) Constructor

Method Summary

Modifier and Type	Method and Description
RuleItem	addCons(int[] itemArray) generates a class association rule out of a given premise.
void	buildDistribution(double conf, double length) updates the distribution of the confidence values.
double	calculatePriorSum(boolean weighted, double mPoint) calculates the numerator and the denominator of the prior equation
java.util.Hashtable	estimatePrior() Method to estimate the prior probabilities
double	findIntervall(double conf) searches the mid point of the interval a given confidence value falls into
void	generateDistribution() Calculates the prior distribution.
double[]	getMidPoints() returns an ordered array of all mid points
java.lang.String	getRevision() Returns the revision string.
static double	logbinomialCoefficient(int upperIndex, int lowerIndex) Method that calculates the base 2 logarithm of a binomial coefficient
double	midPoint(double size, int number) calculates the mid point of an interval
void	midPoints() split the interval [0,1] into a predefined number of intervals and calculates their mid points
int[]	randomCARule(int maxLength, int actualLength, java.util.Random randNum) Constructs an item set of certain length randomly.
int[]	randomRule(int maxLength, int actualLength, java.util.Random randNum) Constructs an item set of certain length randomly.
RuleItem	splitItemSet(int premiseLength, int[] itemArray) splits an item set into premise and consequence and constructs therefore an association rule.
void	updateCounters(ItemSet itemSet) updates the support count of an item set

Methods inherited from class java.lang.Object

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

Constructor Detail

PriorEstimation

public PriorEstimation(Instances instances,
                       int numRules,
                       int numIntervals,
                       boolean car)

Constructor

Parameters:

instances - the instances to be used for generating the associations

numRules - the number of random rules used for generating the prior

numIntervals - the number of intervals to discretise [0,1]

car - flag indicating whether standard or class association rules are mined

Method Detail

generateDistribution

public final void generateDistribution()
                                throws java.lang.Exception

Calculates the prior distribution.

Throws:

java.lang.Exception - if prior can't be estimated successfully

randomRule

public final int[] randomRule(int maxLength,
                              int actualLength,
                              java.util.Random randNum)

Constructs an item set of certain length randomly. This method is used for standard association rule mining.

Parameters:

maxLength - the number of attributes of the instances

actualLength - the number of attributes that should be present in the item set

randNum - the random number generator

Returns:

a randomly constructed item set in form of an int array

randomCARule

public final int[] randomCARule(int maxLength,
                                int actualLength,
                                java.util.Random randNum)

Constructs an item set of certain length randomly. This method is used for class association rule mining.

Parameters:

maxLength - the number of attributes of the instances

actualLength - the number of attributes that should be present in the item set

randNum - the random number generator

Returns:

a randomly constructed item set in form of an int array

buildDistribution

public final void buildDistribution(double conf,
                                    double length)

updates the distribution of the confidence values. For every confidence value the interval to which it belongs is searched and the confidence is added to the confidence already found in this interval.

Parameters:

conf - the confidence of the randomly created rule

length - the legnth of the randomly created rule

findIntervall

public final double findIntervall(double conf)

searches the mid point of the interval a given confidence value falls into

Parameters:

conf - the confidence of a rule

Returns:

the mid point of the interval the confidence belongs to

calculatePriorSum

public final double calculatePriorSum(boolean weighted,
                                      double mPoint)

calculates the numerator and the denominator of the prior equation

Parameters:

weighted - indicates whether the numerator or the denominator is calculated

mPoint - the mid Point of an interval

Returns:

the numerator or denominator of the prior equation

logbinomialCoefficient

public static final double logbinomialCoefficient(int upperIndex,
                                                  int lowerIndex)

Method that calculates the base 2 logarithm of a binomial coefficient

Parameters:

upperIndex - upper Inedx of the binomial coefficient

lowerIndex - lower index of the binomial coefficient

Returns:

the base 2 logarithm of the binomial coefficient

estimatePrior

public final java.util.Hashtable estimatePrior()
                                        throws java.lang.Exception

Method to estimate the prior probabilities

Returns:

a hashtable containing the prior probabilities

Throws:

java.lang.Exception - throws exception if the prior cannot be calculated

midPoints

public final void midPoints()

split the interval [0,1] into a predefined number of intervals and calculates their mid points

midPoint

public double midPoint(double size,
                       int number)

calculates the mid point of an interval

Parameters:

size - the size of each interval

number - the number of the interval. The intervals are numbered from 0 to m_numIntervals.

Returns:

the mid point of the interval

getMidPoints

public final double[] getMidPoints()

returns an ordered array of all mid points

Returns:

an ordered array of doubles conatining all midpoints

splitItemSet

public final RuleItem splitItemSet(int premiseLength,
                                   int[] itemArray)

splits an item set into premise and consequence and constructs therefore an association rule. The length of the premise is given. The attributes for premise and consequence are chosen randomly. The result is a RuleItem.

Parameters:

premiseLength - the length of the premise

itemArray - a (randomly generated) item set

Returns:

a randomly generated association rule stored in a RuleItem

addCons

public final RuleItem addCons(int[] itemArray)

generates a class association rule out of a given premise. It randomly chooses a class label as consequence.

Parameters:

itemArray - the (randomly constructed) premise of the class association rule

Returns:

a class association rule stored in a RuleItem

updateCounters

public final void updateCounters(ItemSet itemSet)

updates the support count of an item set

Parameters:

itemSet - the item set

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision