package ca.pfv.spmf.test;
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.apriori_inverse.AlgoAprioriInverse;
import ca.pfv.spmf.patterns.itemset_array_integers_with_count.Itemsets;
/**
* Example of how to mine perfectly sporadic association rules with AprioriInverse,
* from the source code.
*
* @author Philippe Fournier-Viger (Copyright 2008)
*/
public class MainTestAllPerfectlySporadicAssociationRules_Apriori_saveToMemory {
public static void main(String [] arg) throws IOException{
// Loading the binary context
String input = fileToPath("contextInverse.txt");
String output = null; // null because we want to keep the result into memory instead of save it to a file in this example
// STEP 1: Applying the APRIORI-Inverse algorithm to find sporadic itemsets
// the thresholds that we will use:
double minsup = 0.1;
double maxsup = 0.6;
AlgoAprioriInverse apriori = new AlgoAprioriInverse();
Itemsets patterns = apriori.runAlgorithm(minsup, maxsup, input, null);
int databaseSize = apriori.getDatabaseSize();
patterns.printItemsets(databaseSize);
// STEP 2: Generating all rules from the set of sporadic itemsets (based on Agrawal & Srikant, 94)
double minconf = 0.60;
AlgoAgrawalFaster94 algoAgrawal = new AlgoAgrawalFaster94();
AssocRules rules = algoAgrawal.runAlgorithm(patterns, output,databaseSize, minconf);
rules.printRules(databaseSize);
}
public static String fileToPath(String filename) throws UnsupportedEncodingException{
URL url = MainTestAllPerfectlySporadicAssociationRules_Apriori_saveToMemory.class.getResource(filename);
return java.net.URLDecoder.decode(url.getPath(),"UTF-8");
}
}