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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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/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 3 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program. If not, see .
 *    
 */

package moa.clusterers.outliers.utils.mtree;

import java.util.Set;

import moa.clusterers.outliers.utils.mtree.utils.Pair;

/**
 * An object with partitions a set of data into two sub-sets.
 *
 * @param  The type of the data on the sets.
 */
public interface PartitionFunction {
	
	/**
	 * Executes the partitioning.
	 * 
	 * @param promoted The pair of data objects that will guide the partition
	 *        process.
	 * @param dataSet The original set of data objects to be partitioned.
	 * @param distanceFunction A {@linkplain DistanceFunction distance function}
	 *        to be used on the partitioning.
	 * @return A pair of partition sub-sets. Each sub-set must correspond to one
	 *         of the {@code promoted} data objects.
	 */
	Pair> process(Pair promoted, Set dataSet, DistanceFunction distanceFunction);
	
}