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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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/*
 *    Test.java
 *    Copyright (C) 2013 Aristotle University of Thessaloniki, Greece
 *    @author D. Georgiadis, A. Gounaris, A. Papadopoulos, K. Tsichlas, Y. Manolopoulos
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *    
 *    
 */

package moa.clusterers.outliers.Angiulli;

import moa.streams.ArffFileStream;
import moa.streams.clustering.RandomRBFGeneratorEvents;
import weka.core.Instance;

public class Test {    
    public static void main(String[] args) throws Exception 
    {
        //if (true) return;
        
        int numInstances = 10000;
        
        RandomRBFGeneratorEvents stream = new RandomRBFGeneratorEvents();
        stream.prepareForUse();
        
        //DistanceOutliersAppr myOutlierDetector= new DistanceOutliersAppr();
        ExactSTORM myOutlierDetector= new ExactSTORM();
        myOutlierDetector.queryFreqOption.setValue(1);
        myOutlierDetector.setModelContext(stream.getHeader());
        myOutlierDetector.prepareForUse();  
        
        Long tmStart = System.currentTimeMillis();
        
        int numberSamples = 0;  
        int w = myOutlierDetector.windowSizeOption.getValue();      
        while (stream.hasMoreInstances() && (numberSamples < numInstances)) {
            Instance newInst = stream.nextInstance();
            myOutlierDetector.processNewInstanceImpl(newInst);            
            numberSamples++;
            if (numberSamples % 100 == 0) {
                //System.out.println("Processed " + numberSamples + " stream objects.");  
            }
            if ((numberSamples % (w / 2)) == 0) {
                //myOutlierDetector.PrintOutliers();
            }
        }        
        // myOutlierDetector.PrintOutliers();
        System.out.println("Total time = " + (System.currentTimeMillis() - tmStart) + " ms");
    }
}