/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.clustering.clusterer;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.clustering.ClusterModel;
import com.rapidminer.operator.learner.clustering.FlatCrispClusterModel;
import com.rapidminer.operator.learner.clustering.hierarchical.AgglomerativeClusterer;
import com.rapidminer.operator.learner.clustering.hierarchical.clustersimilarity.ClusterSimilarity;
import com.rapidminer.operator.similarity.DistanceSimilarityConverter;
import com.rapidminer.operator.similarity.SimilarityMeasure;
import com.rapidminer.operator.similarity.SimilarityUtil;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* This operator performs generic agglomorative clustering based on a set of ids and a similarity measure. Clusters are merged as long as their number
* is lower than a given maximum number of clusters. The algorithm implemented here is currently very simple and not very efficient (cubic).
*
* @author Michael Wurst, Ingo Mierswa
* @version $Id: AgglomerativeFlatClusterer.java,v 1.8 2008/09/12 10:31:44 tobiasmalbrecht Exp $
*/
public class AgglomerativeFlatClusterer extends AbstractFlatClusterer {
/** The parameter name for "the maximal number of clusters" */
public static final String PARAMETER_K = "k";
private AgglomerativeClusterer clusterer = new AgglomerativeClusterer();
public AgglomerativeFlatClusterer(OperatorDescription description) {
super(description);
}
public ClusterModel createClusterModel(ExampleSet es) throws OperatorException {
SimilarityMeasure sim = SimilarityUtil.resolveSimilarityMeasure(getParameters(), getInput(), es);
if (sim.isDistance())
sim = new DistanceSimilarityConverter(sim);
ClusterSimilarity csim = AgglomerativeClusterer.resolveClusterSimilarity(getParameters());
FlatCrispClusterModel result = clusterer.clusterFlat(es, sim, csim, getParameterAsInt(PARAMETER_K));
return result;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(SimilarityUtil.generateSimilarityParameter());
types.add(AgglomerativeClusterer.createClusterSimilarityParameter());
ParameterType type = new ParameterTypeInt(PARAMETER_K, "the maximal number of clusters", 2, (int) Double.POSITIVE_INFINITY, 2);
type.setExpert(false);
types.add(type);
return types;
}
}