blob: f4c0d9c7b00f5ebfdd8b99bc53e2ced7077def65 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
|
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) 2012
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 java.util.List;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
/**
* Interface for initializing K-Means
*
* @author Erich Schubert
*
* @param <V> Vector type
*/
public interface KMeansInitialization<V extends NumberVector<V, ?>> {
/**
* Choose initial means
*
* @param relation Relation
* @param k Parameter k
* @param distanceFunction Distance function
* @return List of chosen means for k-means
*/
public abstract List<Vector> chooseInitialMeans(Relation<V> relation, int k, PrimitiveDistanceFunction<? super V, ?> distanceFunction);
}
|
