keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR

Class MODENARProcess

java.lang.Object

keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess

public class MODENARProcess
extends java.lang.Object

It provides the implementation of the MODENAR algorithm to be run in a process

Since:

JDK1.6

Version:

1.1

Author:

Written by Diana Martín (dmartin@ceis.cujae.edu.cu)

Constructor Summary

Constructors

Constructor and Description
MODENARProcess() Default constructor.
MODENARProcess(myDataset ds, int nEvaluations, int popSize, double cr, int threshold, double[] wk, double AF) It creates a new process for the algorithm by setting up its parameters

Method Summary

Modifier and Type	Method and Description
java.util.ArrayList<AssociationRule>	getAssoc_rules_Pareto()
java.util.ArrayList<Chromosome>	getBestrules() Returns the best rules mined.
double	getCR()
myDataset	getDs()
java.lang.String	getParetos()
int	getPopSize()
int	getThreshold()
double[]	getWk()
void	printPareto(java.util.ArrayList<AssociationRule> rulesPareto)
static double	roundDouble(double number, int decimalPlace)
void	run() It runs the algorithm for mining association rules.
void	saveReport(java.util.ArrayList<AssociationRule> rules, java.io.PrintWriter w)
void	setBestrules(java.util.ArrayList<Chromosome> bestrules) Sets the best rules mined with the array given.
void	setCR(double cr)
void	setDs(myDataset ds)
void	setPopSize(int popSize)
void	setThreshold(int threshold)
void	setWk(double[] wk)
boolean	terminate() Checks the stop condition for the algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

MODENARProcess

public MODENARProcess()

Default constructor.

MODENARProcess

public MODENARProcess(myDataset ds,
                      int nEvaluations,
                      int popSize,
                      double cr,
                      int threshold,
                      double[] wk,
                      double AF)

It creates a new process for the algorithm by setting up its parameters

Parameters:

ds - The instance of the dataset for dealing with its records

nEvaluations - The maximum number of evaluations to accomplish before terminating the genetic learning

popSize - The maximum size of population to handle after each generation

threshold - Threshold

wk - Wk values.

cr - The probability of the crossover operator

AF - The parameter which is used while executing the crossover operator

Method Detail

run

public void run()

It runs the algorithm for mining association rules.

terminate

public boolean terminate()

Checks the stop condition for the algorithm.

Returns:

True if the algorithm reachs the stop condition, False otherwise.

getBestrules

public java.util.ArrayList<Chromosome> getBestrules()

Returns the best rules mined.

Returns:

the best rules mined.

setBestrules

public void setBestrules(java.util.ArrayList<Chromosome> bestrules)

Sets the best rules mined with the array given.

Parameters:

bestrules - given best rules to set.

getCR

public double getCR()

setCR

public void setCR(double cr)

getDs

public myDataset getDs()

setDs

public void setDs(myDataset ds)

getPopSize

public int getPopSize()

setPopSize

public void setPopSize(int popSize)

getThreshold

public int getThreshold()

setThreshold

public void setThreshold(int threshold)

getWk

public double[] getWk()

setWk

public void setWk(double[] wk)

roundDouble

public static double roundDouble(double number,
                                 int decimalPlace)

saveReport

public void saveReport(java.util.ArrayList<AssociationRule> rules,
                       java.io.PrintWriter w)

printPareto

public void printPareto(java.util.ArrayList<AssociationRule> rulesPareto)

getParetos

public java.lang.String getParetos()

getAssoc_rules_Pareto

public java.util.ArrayList<AssociationRule> getAssoc_rules_Pareto()