package ca.pfv.spmf.test;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.UnsupportedEncodingException;
import java.net.URL;
import ca.pfv.spmf.algorithms.associationrules.agrawal94_association_rules.AlgoAgrawalFaster94;
import ca.pfv.spmf.algorithms.associationrules.agrawal94_association_rules.AssocRules;
import ca.pfv.spmf.algorithms.frequentpatterns.fpgrowth.AlgoFPGrowth;
import ca.pfv.spmf.patterns.itemset_array_integers_with_count.Itemsets;
/**
* Example of how to mine all association rules with FPGROWTH,
* from the source code.
*
* @author Philippe Fournier-Viger (Copyright 2008)
*/
public class MainTestAllAssociationRules_FPGrowth_saveToMemory {
public static void main(String [] arg) throws FileNotFoundException, IOException{
// Loading the binary context
String input = fileToPath("contextIGB.txt");
// STEP 1: Applying the FP-GROWTH algorithm to find frequent itemsets
double minsupp = 0.5;
AlgoFPGrowth fpgrowth = new AlgoFPGrowth();
Itemsets patterns = fpgrowth.runAlgorithm(input, null, minsupp);
int databaseSize = fpgrowth.getDatabaseSize();
patterns.printItemsets(databaseSize);
// STEP 2: Generating all rules from the set of frequent itemsets (based on Agrawal & Srikant, 94)
double minconf = 0.60;
AlgoAgrawalFaster94 algoAgrawal = new AlgoAgrawalFaster94();
// the next line run the algorithm.
// Note: we pass null as output file path, because we don't want
// to save the result to a file, but keep it into memory.
AssocRules rules = algoAgrawal.runAlgorithm(patterns, null, databaseSize, minconf);
rules.printRules(databaseSize);
System.out.println("DATABASE SIZE " + databaseSize);
}
public static String fileToPath(String filename) throws UnsupportedEncodingException{
URL url = MainTestAllAssociationRules_FPGrowth_saveToMemory.class.getResource(filename);
return java.net.URLDecoder.decode(url.getPath(),"UTF-8");
}
}