import weka.associations.Apriori; //导入依赖的package包/类
public static String buildAssociate() throws Exception {
    InstanceQuery query = new InstanceQuery();
    query.setUsername("root");
    query.setPassword("cs6310");
    query.setDatabaseURL("jdbc:mysql://localhost/system?#characterEncoding=UTF-8");
    query.setQuery("select * from courses_sessions;");
    // You can declare that your data set is sparse
    // query.setSparseData(true);
    Instances data = query.retrieveInstances();
    data.setClassIndex(data.numAttributes() - 1);

    final NumericToNominal filter = new NumericToNominal();

    filter.setInputFormat(data);
    data = Filter.useFilter(data, filter);
    if (data.size() > 0) {
        // build associator
        Apriori apriori = new Apriori();
        apriori.setClassIndex(data.classIndex());
        apriori.buildAssociations(data);
        return String.valueOf(apriori);
    } else {
        return "Not enough data provided";
    }
}
 

import weka.associations.Apriori; //导入依赖的package包/类
/**
 * Get classifier's class name by a short name
 * */
public static String getClassifierClassName(String classifierName) {
	String className = "";
	switch (classifierName) {
	case "SGD":
		className = SGD.class.toString();
		break;
	case "SGDText":
		className = SGDText.class.toString();
		break;
	case "J48":
		className = J48.class.toString();
		break;
	case "PART":
		className = PART.class.toString();
		break;
	case "NaiveBayes":
		className = NaiveBayes.class.toString();
		break;
	case "NBUpdateable":
		className = NaiveBayesUpdateable.class.toString();
		break;
	case "AdaBoostM1":
		className = AdaBoostM1.class.toString();
		break;
	case "LogitBoost":
		className = LogitBoost.class.toString();
		break;
	case "Bagging":
		className = Bagging.class.toString();
		break;
	case "Stacking":
		className = Stacking.class.toString();
		break;
	case "AdditiveRegression":
		className = AdditiveRegression.class.toString();
		break;
	case "Apriori":
		className = Apriori.class.toString();
		break;
	default:
		className = SGD.class.toString();
	}
	className = className.substring(6);
	return className;
}
 

import weka.associations.Apriori; //导入依赖的package包/类
public static void main(String args[]) throws Exception {
	// load data
	Instances data = new Instances(new BufferedReader(new FileReader("data/supermarket.arff")));
	// build model
	Apriori model = new Apriori();
	model.buildAssociations(data);
	System.out.println(model);
	
	FPGrowth fpgModel = new FPGrowth();
	fpgModel.buildAssociations(data);
	System.out.println(fpgModel);
	
	
}
 

import weka.associations.Apriori; //导入依赖的package包/类
public void generateRule(){
		apriori = new Apriori();
		try {
//			apriori.setNumRules(20);
			apriori.buildAssociations(superMarket);
			System.out.println(apriori);
		} catch (Exception e) {
		}
	}
 

import weka.associations.Apriori; //导入依赖的package包/类
public DataMiningControl() {
    this.selectedTag = new SelectedTag(0, Apriori.TAGS_SELECTION); //confiddence ref weka.Apriori.
    this.minMetric = Constantes.CONFIANCA_MIN_PADRAO;
    this.minSup = Constantes.SUPORTE_MIN_PADRAO;
    this.verboseMode = Constantes.VERBOSE_MODE;
    this.maxRules = Constantes.MAX_RULES;
}
 

import weka.associations.Apriori; //导入依赖的package包/类
public static String[] getPossibleMetricTypes() {
    String[] metricTypes = new String[Apriori.TAGS_SELECTION.length];
    for (int i = 0; i < metricTypes.length; i++) {
        metricTypes[i] = Apriori.TAGS_SELECTION[i].getReadable();
    }

    return metricTypes;
}
 

import weka.associations.Apriori; //导入依赖的package包/类
public void setMetricType(int tagID) {
    try {
        TAGS_SELECTION[tagID].getID();
    } catch (ArrayIndexOutOfBoundsException ex) {
        throw new RuntimeException("TagId not known. Metric's TagId type must be one of: 0, 1, 2, 3");
    }
    this.setSelectedTag(new SelectedTag(tagID, Apriori.TAGS_SELECTION));
}