List of buildAssociations() Examples

Examples of buildAssociations()

weka.associations.Apriori.buildAssociations()
Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence. @param instances the instances to be used for generating the associations @throws Exception if rules can't be built successfully
weka.associations.Associator.buildAssociations()
Generates an associator. Must initialize all fields of the associator that are not being set via options (ie. multiple calls of buildAssociator must always lead to the same result). Must not change the dataset in any way. @param data set of instances serving as training data @exception Exception if the associator has not been generated successfully
weka.associations.FPGrowth.buildAssociations()
Method that generates all large item sets with a minimum support, and from these all association rules with a minimum metric (i.e. confidence, lift etc.). @param data the instances to be used for generating the associations @throws Exception if rules can't be built successfully

Examples of weka.associations.Apriori.buildAssociations()

  public void trainApriori(Instances data) { 
    Apriori nb = new Apriori();
    nb.setUpperBoundMinSupport(0.8);
    
      try {
      nb.buildAssociations(data);
    } catch (Exception e) {
      e.printStackTrace();
    }
    
      System.out.println(nb);

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Examples of weka.associations.Associator.buildAssociations()

            TupleAssociator tupleAssociator = 
                new TupleAssociator(
                        associator, 
                        WekaUtilities.getAttributes(dataset));
           
            associator.buildAssociations(dataset);
            mOutputAssociator.write(tupleAssociator.getAssociator());
            if(mOutputRules != null)
              mOutputRules.write(tupleAssociator.getAssociator().toString());
            
        }

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Examples of weka.associations.Associator.buildAssociations()

            TupleAssociator tupleAssociator = 
                new TupleAssociator(
                        associator, 
                        WekaUtilities.getAttributes(dataset));
           
            associator.buildAssociations(dataset);
            mOutputAssociator.write(ControlBlock.LIST_BEGIN);
            mOutputAssociator.write(tupleAssociator.getAssociator());
            mOutputAssociator.write(ControlBlock.LIST_END);
            if(mOutputRules != null && ((AssociationRulesProducer)tupleAssociator.getAssociator()).getAssociationRules().getRules().size() > 0 ) {
              mOutputRules.write(ControlBlock.LIST_BEGIN);

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Examples of weka.associations.Associator.buildAssociations()

      m_History.addResult(name, outBuff);
      m_History.setSingle(name);
      
      // Build the model and output it.
      m_Log.statusMessage(Messages.getInstance().getString("AssociationsPanel_StartAssociator_Run_Log_StatusMessage_Text_Second"));
      associator.buildAssociations(inst);
      outBuff.append(Messages.getInstance().getString("AssociationsPanel_StartAssociator_Run_OutBuffer_Text_Nineth"));
      outBuff.append(associator.toString() + '\n');
      m_History.updateResult(name);
      m_Log.logMessage(Messages.getInstance().getString("AssociationsPanel_StartAssociator_Run_Log_LogMessage_Text_Third") + cname);
      m_Log.statusMessage(Messages.getInstance().getString("AssociationsPanel_StartAssociator_Run_Log_StatusMessage_Text_Third"));

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Examples of weka.associations.Associator.buildAssociations()

      m_History.addResult(name, outBuff);
      m_History.setSingle(name);
      
      // Build the model and output it.
      m_Log.statusMessage("Building model on training data...");
      associator.buildAssociations(inst);
      outBuff.append("=== Associator model (full training set) ===\n\n");
      outBuff.append(associator.toString() + '\n');
      m_History.updateResult(name);
      m_Log.logMessage("Finished " + cname);
      m_Log.statusMessage("OK");

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Examples of weka.associations.Associator.buildAssociations()

      m_History.addResult(name, outBuff);
      m_History.setSingle(name);
      
      // Build the model and output it.
      m_Log.statusMessage("Building model on training data...");
      associator.buildAssociations(inst);
      outBuff.append("=== Associator model (full training set) ===\n\n");
      outBuff.append(associator.toString() + '\n');
      m_History.updateResult(name);
      if (m_storeOutput.isSelected()) {
        if (associator instanceof Drawable) {

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Examples of weka.associations.FPGrowth.buildAssociations()

    FPGrowth fp = new FPGrowth();
    fp.setLowerBoundMinSupport(5.0);
    fp.setNumRulesToFind(100000);
    fp.setUpperBoundMinSupport(1.0);
    fp.setMinMetric(.0);
    fp.buildAssociations(inst);
    List<FPGrowth.AssociationRule> rules = fp.getAssociationRules();


    Set fpgrowth = new TreeSet();
    String pattern;
    PrintWriter writer;

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