ca.pfv.spmf.algorithms.associationrules.TopKRules_and_TNR

Class AlgoTNR

java.lang.Object

ca.pfv.spmf.algorithms.associationrules.TopKRules_and_TNR.AlgoTNR

public class AlgoTNR
extends java.lang.Object

TNR is an algorithm for mining the TOP-K non redundant association rules with a pattern growth approach and several optimizations. This is the original implementation as proposed in the following paper:

Fournier-Viger, P., Tseng, V.S. (2012). Mining Top-K Non-Redundant Association Rules. Proc. 20th International Symposium on Methodologies for Intelligent Systems (ISMIS 2012), Springer, LNCS 7661, pp. 31- 40.

Constructor Summary

Constructors

Constructor and Description
AlgoTNR() Default constructor

Method Summary

Modifier and Type	Method and Description
void	printStats() Print statistics about the last algorithm execution.
RedBlackTree<RuleG>	runAlgorithm(int k, double minConfidence, Database database, int delta) Run the algorithm.
void	writeResultTofile(java.lang.String path) Write the rules found to an output file.

Methods inherited from class java.lang.Object

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

Constructor Detail

AlgoTNR

public AlgoTNR()

Default constructor

Method Detail

runAlgorithm

public RedBlackTree<RuleG> runAlgorithm(int k,
                                        double minConfidence,
                                        Database database,
                                        int delta)

Run the algorithm.

Parameters:

k - the value of k.

minConfidence - the minimum confidence threshold.

database - the database.

delta - the delta parameter

Returns:

a RedBlackTree containing approximately k rules.

writeResultTofile

public void writeResultTofile(java.lang.String path)
                       throws java.io.IOException

Write the rules found to an output file.

Parameters:

path - the path to the output file

Throws:

java.io.IOException - exception if an error while writing the file

printStats

public void printStats()

Print statistics about the last algorithm execution.