kmeans.java

人工智能技术K均值算法

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 * Version: MPL 1.1/GPL 2.0/LGPL 2.1
 *
 * The contents of this file are subject to the Mozilla Public License Version
 * 1.1 (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.mozilla.org/MPL/
 *
 * Software distributed under the License is distributed on an "AS IS" basis,
 * WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
 * for the specific language governing rights and limitations under the
 * License.
 *
 * The Original Code is EDAM Enchilada's KMeans class.
 *
 * The Initial Developer of the Original Code is
 * The EDAM Project at Carleton College.
 * Portions created by the Initial Developer are Copyright (C) 2005
 * the Initial Developer. All Rights Reserved.
 *
 * Contributor(s):
 * Ben J Anderson [e-mail]
 * David R Musicant [e-mail]
 * Anna Ritz [e-mail]
 *
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/*
 * Created on Aug 19, 2004
 *
 * TODO: May need to create tables in Database to store the atom
 * id's of the particles belonging to each centroid to avoid 
 * blowing out memory
 */
package analysis.clustering;

import java.util.ArrayList;
import database.InfoWarehouse;

/**
 * KMeans uses the mean to determine the new centroids.  
 * 
 * @author andersbe
 *
 */
public class KMeans extends ClusterK 
{
	/**
	 * Constructor.  Calls the constructor for ClusterK.
	 * @param cID - collection ID
	 * @param database - database interface
	 * @param k - number of centroids desired
	 * @param name - collection name
	 * @param comment -comment to enter
	 */
	public KMeans(int cID, InfoWarehouse database, int k,
			String name, String comment, boolean refine, ClusterInformation c) 
			{
				super(cID, database, k, 
						name.concat("KMeans"), comment, refine, c);
	}

	/** 
	 * method necessary to extend from ClusterK.  Begins the clustering
	 * process.
	 * @param - interactive or testing mode - christej
	 * @return - new collection int.
	 */
	public int cluster(boolean interactive) {
		if(interactive)
			return divide();
		else
			return innerDivide(interactive);
	}

	/**
	 * This code has been removed; it is much more efficient to accumulate
	 * a cluster as you go.
	 * @author dmusican
	 * 
	 */
	public Centroid averageCluster(
			Centroid thisCentroid,
			ArrayList<Integer> particlesInCentroid) {
		throw new UnsupportedOperationException("Averaging a k-means cluster is inefficient.");
	}
}