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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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/*
 *    MTreeStreamObjects.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.MCOD;

import java.util.Set;
import moa.clusterers.outliers.utils.mtree.ComposedSplitFunction;
import moa.clusterers.outliers.utils.mtree.DistanceFunction;
import moa.clusterers.outliers.utils.mtree.DistanceFunctions;
import moa.clusterers.outliers.utils.mtree.MTree;
import moa.clusterers.outliers.utils.mtree.PartitionFunctions;
import moa.clusterers.outliers.utils.mtree.PromotionFunction;
import moa.clusterers.outliers.utils.mtree.utils.Pair;
import moa.clusterers.outliers.utils.mtree.utils.Utils;

class MTreeStreamObjects extends MTree {

    private static final PromotionFunction nonRandomPromotion = new PromotionFunction() {

        @Override
        public Pair process(Set dataSet, DistanceFunction distanceFunction) {
            return Utils.minMax(dataSet);
        }
    };

    MTreeStreamObjects() {
        super(2, DistanceFunctions.EUCLIDEAN,
                new ComposedSplitFunction(
                nonRandomPromotion,
                new PartitionFunctions.BalancedPartition()));
    }

    public void add(StreamObj data) {
        super.add(data);
        _check();
    }

    public boolean remove(StreamObj data) {
        boolean result = super.remove(data);
        _check();
        return result;
    }

    DistanceFunction getDistanceFunction() {
        return distanceFunction;
    }
};