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package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
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/>.
*/
import de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
/**
* Some constants and options shared among kmeans family algorithms.
*
* @author Erich Schubert
*
* @param <V> Number vector type
* @param <M> Actual model type
*/
public interface KMeans<V extends NumberVector, M extends Model> extends ClusteringAlgorithm<Clustering<M>>, DistanceBasedAlgorithm<V> {
/**
* Parameter to specify the initialization method
*/
public static final OptionID INIT_ID = new OptionID("kmeans.initialization", "Method to choose the initial means.");
/**
* Parameter to specify the number of clusters to find, must be an integer
* greater than 0.
*/
public static final OptionID K_ID = new OptionID("kmeans.k", "The number of clusters to find.");
/**
* Parameter to specify the number of clusters to find, must be an integer
* greater or equal to 0, where 0 means no limit.
*/
public static final OptionID MAXITER_ID = new OptionID("kmeans.maxiter", "The maximum number of iterations to do. 0 means no limit.");
/**
* Parameter to specify the random generator seed.
*/
public static final OptionID SEED_ID = new OptionID("kmeans.seed", "The random number generator seed.");
/**
* Run the clustering algorithm.
*
* @param database Database to run on.
* @param rel Relation to process.
* @return Clustering result
*/
Clustering<M> run(Database database, Relation<V> rel);
/**
* Set the value of k. Needed for some types of nested k-means.
*
* @param k K parameter
*/
void setK(int k);
/**
* Set the distance function to use.
*
* @param distanceFunction Distance function.
*/
void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction);
}
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